Tool For Identification Research Articles

Digital health interventions for remote follow-up after mild traumatic brain injury.

After a mild traumatic brain injury (mild TBI,) a significant number of patients may experience persistent symptoms and disabilities for months to years. Early identification and timely management of persistent symptoms may help to reduce the long-term impacts of mild TBIs. There is currently no formalised method for identifying patients with persistent symptoms after mild TBI once they are discharged from emergency department. Assess the feasibility of a remote monitoring tool for early identification of persistent symptoms after mild TBI in the outpatient setting using digital tools. Electronic surveys were sent to patients with mild TBI who presented to the emergency department at a Major Trauma Centre in England. The surveys were completed at three different timepoints (within days of injury (S1), 1 month (S2), and 3 months (S3) after injury). The indicators used to assess feasibility were engagement, number of eligible patients for follow-up evidence of need for the intervention, and consistency with the literature. Feedback was sought from participants. Of the 200 people invited to participate, 134 (67.0%) completed S1, 115 (57.5%) completed S2, and 95 (47.5%) completed S3. The rates of persistent symptoms ranged from 17.9%-62.6% depending on the criteria used, and we found a significant proportion of the participants experienced morbidity 1 and 3 months after injury. The electronic follow-up tool was deemed an acceptable and user-friendly method for service delivery by participants. Using digital tools to monitor and screen mild TBI patients for persistent symptoms is feasible. This could be a scalable, cost-effective, and convenient solution which could improve access to healthcare and reduce healthcare inequalities. This could enable early identification of patients with further medical needs and facilitate timely intervention to improve the clinical workflows, patient satisfaction, and health outcomes for people with persistent morbidities after mild TBIs.

Network pharmacology speaking to ethnopharmacology: new data on an ancient remedy

Network pharmacology as a “green approach”, predicting metabolite behaviours chemically and biologically and guid¬ing biological experimental design, is a new strategy aiming to uncover the mechanism of action of natural products as drug candidates. It provides a powerful way to identify novel mechanisms of natural products with potential thera¬peutic effects. This approach has emerged as a powerful tool to overcome the limitations of traditional methods, such as the ability to predict the adverse effects of a drug and the likelihood of failure during clinical trials, by applying systems biology principles to the field of pharmacology. This method combines the multi-omics dataset, computer modeling, and chemical biology so as to reveal pharmaceutical actions and guide drug discovery. Therefore, computer-aided drug design combined with network pharmacology can be viewed as a novel in silico screening ap¬proach to drug discovery, by utilising chemoinformatics, bioinformatics, structure biology, and chemical biology. This strategy includes target-based virtual screening - molecular docking, ligand similarity-based virtual screening, and inverse screening (Inver-dock), providing a powerful tool for target identification of drug candidates, multitarget dis¬covery, and natural bioactive product profiling. It can also be used for selectivity profiling of drugs, drug repositioning, safety profiling, and metabolism profiling prediction (ADMET).

Criminal Investigation Tracker with Suspect Prediction

This initiative seeks to craft an inclusive and intuitive web-based platform tailored for criminal identification via image and video surveillance, leveraging cutting-edge facial recognition technology. The system integrates a registration portal for inputting data and images of known criminals, utilizing OpenCV and advanced facial recognition algorithms to securely analyze and store their facial attributes. In addition to allowing users to upload images for analysis, the system offers immediate feedback on potential matches with registered criminals. Moreover, the video surveillance module extends this capability to short videos, employing video analytics to identify faces within the footage. The platform ensures real-time feedback for successful identifications and provides an advanced feature enabling users to download details of identified criminals in an Excel format. By amalgamating state-of-the-art technology with an intuitive interface, this project endeavors to bolster law enforcement endeavors by furnishing an efficient and precise tool for criminal identification and tracking. Its objectives encompass developing a robust system for identifying and tracking criminals through advanced facial recognition algorithms and OpenCV technology, designing a user-friendly web interface for seamless navigation across various modules, and establishing a secure and efficient registration section for compiling comprehensive databases of facial features.

Acceptability and feasibility of screening with a pediatric care provider-led social determinants of health identification tool

BackgroundComplex social determinants of health may not be easily recognized by health care providers and pose a unique challenge in the vulnerable pediatric population where patients may not be able to advocate for themselves. The goal of this study was to examine the acceptability and feasibility of health care providers using an integrated brief pediatric screening tool in primary care and hospital settings.MethodsThe framework of the Child and Adolescent Needs and Strengths (CANS) and Pediatric Intermed tools was used to inform the selection of items for the 9-item Child and Adolescent Needs and Strengths-Pediatric Complexity Indicator (CANS-PCI). The tool consisted of three domains: biological, psychological, and social. Semi-structured interviews were conducted with health care providers in pediatric medical facilities in Ottawa, Canada. A low inference and iterative thematic synthesis approach was used to analyze the qualitative interview data specific to acceptability and feasibility.ResultsThirteen health care providers participated in interviews. Six overarching themes were identified: acceptability, logistics, feasibility, pros/cons, risk, and privacy. Overall, participants agreed that a routine, trained provider-led pediatric tool for the screening of social determinants of health is important (n = 10, 76.9%), acceptable (n = 11; 84.6%), and feasible (n = 7, 53.8%).InterpretationThough the importance of social determinants of health are widely recognized, there are limited systematic methods of assessing, describing, and communicating amongst health care providers about the biomedical and psychosocial complexities of pediatric patients. Based on this study’s findings, implementation of a brief provider-led screening tool into pediatric care practices may contribute to this gap.

Harnessing Consumer Wearable Digital Biomarkers for Individualized Recognition of Postpartum Depression Using the All of Us Research Program Data Set: Cross-Sectional Study.

Postpartum depression (PPD) poses a significant maternal health challenge. The current approach to detecting PPD relies on in-person postpartum visits, which contributes to underdiagnosis. Furthermore, recognizing PPD symptoms can be challenging. Therefore, we explored the potential of using digital biomarkers from consumer wearables for PPD recognition. The main goal of this study was to showcase the viability of using machine learning (ML) and digital biomarkers related to heart rate, physical activity, and energy expenditure derived from consumer-grade wearables for the recognition of PPD. Using the All of Us Research Program Registered Tier v6 data set, we performed computational phenotyping of women with and without PPD following childbirth. Intraindividual ML models were developed using digital biomarkers from Fitbit to discern between prepregnancy, pregnancy, postpartum without depression, and postpartum with depression (ie, PPD diagnosis) periods. Models were built using generalized linear models, random forest, support vector machine, and k-nearest neighbor algorithms and evaluated using the κ statistic and multiclass area under the receiver operating characteristic curve (mAUC) to determine the algorithm with the best performance. The specificity of our individualized ML approach was confirmed in a cohort of women who gave birth and did not experience PPD. Moreover, we assessed the impact of a previous history of depression on model performance. We determined the variable importance for predicting the PPD period using Shapley additive explanations and confirmed the results using a permutation approach. Finally, we compared our individualized ML methodology against a traditional cohort-based ML model for PPD recognition and compared model performance using sensitivity, specificity, precision, recall, and F1-score. Patient cohorts of women with valid Fitbit data who gave birth included <20 with PPD and 39 without PPD. Our results demonstrated that intraindividual models using digital biomarkers discerned among prepregnancy, pregnancy, postpartum without depression, and postpartum with depression (ie, PPD diagnosis) periods, with random forest (mAUC=0.85; κ=0.80) models outperforming generalized linear models (mAUC=0.82; κ=0.74), support vector machine (mAUC=0.75; κ=0.72), and k-nearest neighbor (mAUC=0.74; κ=0.62). Model performance decreased in women without PPD, illustrating the method's specificity. Previous depression history did not impact the efficacy of the model for PPD recognition. Moreover, we found that the most predictive biomarker of PPD was calories burned during the basal metabolic rate. Finally, individualized models surpassed the performance of a conventional cohort-based model for PPD detection. This research establishes consumer wearables as a promising tool for PPD identification and highlights personalized ML approaches, which could transform early disease detection strategies.

A new bacterial consortia for management of Fusarium head blight in wheat

Fusarium head blight (FHB) is a significantly important disease in cereals primarily caused by Fusarium species. FHB control is largely executed through chemical strategies, which are costlier to sustainable wheat production, resulting in leaning towards sustainable sources such as resistance breeding and biological control methods for FHB. The present investigation was aimed at evaluating newly identified bacterial consortium (BCM) as biocontrol agents for FHB and understanding the morpho-physiological traits associated with the disease resistance of spring wheat. Preliminary evaluation through antagonistic plate assay and in vivo assessment indicated that BCM effectively inhibited Fusarium growth in spring wheat, reducing area under disease progress curve (AUDPC) and deoxynivalenol (DON), potentially causing type II and V resistance, and improving single spike yield (SSPY). Endurance to FHB infection with the application of BCM is associated with better sustenance of spike photosynthetic performance by improving the light energy harvesting and its utilization. Correlation and path-coefficient analysis indicated that maximum quantum yield (QY_max) is directly influencing the improvement of SSPY and reduction of grain DON accumulation, which is corroborated by principal component analysis. The chlorophyll fluorescence traits identified in the present investigation might be applied as a phenotyping tool for the large-scale identification of wheat sensitivity to FHB.

Abstract PO2-11-10: Can chemotherapy-induced peripheral neuropathy be predicted? Implications for future prevention and treatment of this side-effect

Abstract Background: The pain and sensory abnormalities associated with chemotherapy-induced peripheral neuropathy (CIPN) may persist for months or even years after chemotherapy. CIPN has a negative impact on routine activities, functions, and behaviors in the domestic, work, and social lives of cancer patients, adversely affecting the quality of their survivorship. CIPN is a major adverse effect of taxanes and other agents, possibly requiring dose-reduction or early termination of treatment. Taxane-based regimens are first-line treatment in both early-stage and metastatic breast cancer and therefore place numerous women at risk for developing CIPN. Regrettably, taxane-induced neurotoxicity can be arduous to predict, and there are no preventive or curative treatments currently available. Our work aims to assess a predictive tool for identification of patients at high risk of developing neuropathy and the progression of symptoms towards chronic CIPN. We hypothesized that oxidative stress might be a triggering event for the development of CIPN and that changes in glutathione recycling can identify patients at high risk for developing prolonged and severe CIPN. This hypothesis is based on reports in the literature proposing that CIPN is triggered by damage to the myelin sheath, which protects nerves from damage, by drug-induced free radicals in and around the nerves. Damage to myelin, a lipid- and protein rich sheath, by lipid peroxidation can result in loss of signal transmission, false signaling, or signal overload. Prolonged exposure to elevated levels of free radicals might result in structural changes to the nerves. Methods: Cancer patients seeking treatment at the Lankenau Medical Center Cancer center were asked to participate in our Institutional Review Board approved study of adverse effects. In this on-going study, the cohort (Ntotal =352) includes 104 breast cancer patients that are predominantly Caucasian with 14.9% African American. The median age is 56 (range 26 – 82) and 60.9% were treated with a taxane-based regimen. Nearly half the breast cancer group were diagnosed with Stage I disease. All consented patients donated a tube of blood prior to each treatment and filled out the Rotterdam Symptom Checklist (RSCL). Blood samples were analyzed for glutathione recycling using the ChemoTox assay (MNT™ Test, MYNARI Biomedical) and lipid peroxidation using a thiobarbituric acid reactive substances (TBARS) test (Cayman Chemical). All samples were analyzed in duplicate, and results analyzed using GraphPad Prism 8.4.3. Results: Preliminary results from the first 83 breast cancer patients showed African American patients reported a higher rate of NCCN grade 2 and 3 CIPN while Caucasians reported a higher rate of severe, long-lasting CIPN. Our results showed that patients who reported CIPN at later cycles had diminished glutathione recycling capacity already after the first treatment. Drop in recycling capacity had an inverse relationship with lipid peroxidation and grade of CIPN. Both decreased recycling capacity and increase in lipid oxidation were apparent already after the first treatment in patients that reported CIPN after cycle 6-8 or after completion of treatment Discussion: There are many proposed mechanisms for the development of CIPN. One mechanism that is shared by both most agents is damage induced by free radicals released following chemotherapy. We show that patients with reduced ability to neutralize free radicals have elevated and prolonged lipid peroxidation. Both processes precede the development of CIPN by several treatment cycles. Therefore, this test could identify patients that might benefit from other regimens rather than a taxane-based therapy. The test could also aid in the identification of patients for preventive therapy once such agents become available. Citation Format: Alisha Maity, Nolan Metz, Kathryn Fleck, Zonera Ali, Aarthi Shevade, Arezoo Ghaneie, Margaretha Wallon. Can chemotherapy-induced peripheral neuropathy be predicted? Implications for future prevention and treatment of this side-effect [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-11-10.

An updated list of eumycetoma causative agents and their differences in grain formation and treatment response.

SUMMARYIn 2023, the World Health Organization designated eumycetoma causative agents as high-priority pathogens on its list of fungal priority pathogens. Despite this recognition, a comprehensive understanding of these causative agents is lacking, and potential variations in clinical manifestations or therapeutic responses remain unclear. In this review, 12,379 eumycetoma cases were reviewed. In total, 69 different fungal species were identified as causative agents. However, some were only identified once, and there was no supporting evidence that they were indeed present in the grain. Madurella mycetomatis was by far the most commonly reported fungal causative agent. In most studies, identification of the fungus at the species level was based on culture or histology, which was prone to misidentifications. The newly used molecular identification tools identified new causative agents. Clinically, no differences were reported in the appearance of the lesion, but variations in mycetoma grain formation and antifungal susceptibility were observed. Although attempts were made to explore the differences in clinical outcomes based on antifungal susceptibility, the lack of large clinical trials and the inclusion of surgery as standard treatment posed challenges in drawing definitive conclusions. Limited case series suggested that eumycetoma cases caused by Fusarium species were less responsive to treatment than those caused by Madurella mycetomatis. However, further research is imperative for a comprehensive understanding.

Morphometric Analysis of Hip Bones for Gender Identification in Unexpected Death Cases in Kabul, Afghanistan

Introduction: Unexpected deaths in Kabul Afghanistan often involve victims who cannot be identified due to facial disfigurement or missing identity documents. Gender identification is an important step in the forensic identification process, especially in cultural and religious contexts that have strict norms regarding gender. Pelvic bone morphometric analysis offers an accurate and non-invasive method for gender identification. This study aims to evaluate the effectiveness of hip bone morphometric analysis in identifying gender in cases of unexpected death in Kabul, Afghanistan. Methods: The samples consisted of 100 human pelvises, 50 of each gender, collected from skeletal remains at Kabul hospitals and burial grounds. Pelvic dimensions were measured using calibration techniques and statistical analysis was performed to identify significant morphometric differences between the gender. Results: Morphometric analysis showed significant differences in several pelvic dimensions between men and women. The most informative dimensions for gender identification are pelvic width, pelvic height, and superior diameter of the acetabulum. The prediction model developed using these dimensions achieved a gender identification accuracy of 94%. Conclusion: Pelvic bone morphometric analysis can be a valuable tool for forensic identification in cases of unexpected deaths in Kabul Afghanistan. This method offers high accuracy, is non-invasive and easy to apply, and can help in the process of identifying victims and resolving legal cases.

First Report of a 'Candidatus Phytoplasma sacchari'-Related Strain Associated with Yellowing and Decline of Silver Bluestem in Texas, U.S.A.

Silver bluestem [Bothriochloa laguroides (DC.) Herter] is a warm-season grass native to Texas. This perennial grass plays a crucial role in maintaining ecological balance and supporting wildlife in the region. In September 2022, while investigating the ecological impact of invasive grass species on a grassland located near Pipe Creek (TX), B. laguroides plants were observed showing symptoms that included yellowing of the blades and occasionally brown discoloration of the midveins and stems (Fig. S1). Disease incidence was estimated as 2% of silver bluestem plants in the 2 hectares surveyed. To investigate the possibility of a phytoplasma association with the symptoms, four symptomatic and four asymptomatic leaf samples were collected for further study. Total DNA was extracted from leaf midribs using a DNeasy Plant Mini Kit (Qiagen). The DNA extracts were tested using a phytoplasma-specific quantitative PCR assay (Hodgetts et al. 2009), which identified two out of the four symptomatic B. laguroides samples as positive for phytoplasmas. A semi-nested PCR assay for amplification of the 16S rRNA gene fragment was then performed on these samples with primers P1/16S-SR followed by P1A/16S-SR (Deng, and Hiruki 1991; Lee et al. 2004), and two additional housekeeping genes (tuf and secA) were amplified as previously described (Makarova et al. 2012; Hodgetts et al. 2008; Bekele et al. 2011). All amplicons of the expected size, 1.5 kb (16S rRNA), 0.4 kb (tuf) and 0.6 kb (secA), were purified and bi-directionally sequenced using primers from each gene second round PCR amplification. Analysis of the sequences derived from the three gene fragments revealed no variation between the two plant samples and confirmed they originated from a phytoplasma, termed strain TXSB-2 (Texas Silver Bluestem). Sequences from a single B. laguroides plant DNA extract were deposited in GenBank with accession numbers OR711913 (16S rRNA), OR709687 (tuf) and OR709688 (secA). A BLAST search of the 16S rRNA gene sequence from TXSB-2 against the NCBI nucleotide database, showed 99.58% sequence identity with an unclassified phytoplasma clone 139-1 from a leafhopper collected in Australia (MW281491) (Fig. S2). The partial nucleotide sequence of the tuf and secA genes showed 90.60% and 89.78% similarity, respectively, to the corresponding genes in 'Ca. P. sacchari' strain SCWL1 (CP115156) associated with sugarcane in China. The iPhyClassifier, an interactive online tool for phytoplasma identification and classification (Zhao et al. 2009), was used to determine the 'Candidatus Phytoplasma' species affiliation and group/subgroup classification status of this phytoplasma strain. The result showed that the TXSB-2 16S rDNA shared 98.94% sequence identity with that of the 'Ca. P. sacchari' reference strain (GenBank accession: MN889545), indicating TXSB-2 is a 'Ca. P. sacchari'-related strain. The result from virtual restriction fragment length polymorphism (RFLP) analysis of the 16S rDNA F2nR2 fragment revealed that TXSB-2 possessed a collective RFLP pattern that is distinct from the reference patterns of all established phytoplasma ribosomal subgroups and is proposed as the representative strain of a new subgroup designated as 16SrXI-H. 'Candidatus Phytoplasma sacchari' has been reported associated with sugarcane grassy shoot disease, which is considered among the most damaging diseases of sugarcane across parts of Southeast Asia and India (Kirdat et al. 2021). The same phytoplasma was recently confirmed infecting sorghum in India (Nithya et al. 2024). To our knowledge, this is the first report of a 'Ca. P. sacchari'-related strain infecting B. laguroides in the United States. Moreover, B. laguroides is a new host for strains related to 'Ca. P. sacchari'. Further investigation is required to elucidate the prevalence of this disease in the area, its natural vectors, and the potential consequences arising from this novel phytoplasma strain within its ecosystem in Texas.

Early prediction of sepsis-induced respiratory tract infection using a biomarker-based machine-learning algorithm.

Early and differential diagnosis of sepsis is essential to avoid unnecessary antibiotic use and further reduce patient morbidity and mortality. Here, we aimed to identify predictors of sepsis and advance a machine-learning strategy to predict sepsis-induced respiratory tract infection (RTI). Patients with sepsis and RTI were selected via retrospective analysis, and essential population characteristics and laboratory parameters were recorded. To improve the performance of the primary model and avoid over-fitting, a recursive feature elimination with cross-validation (RFECV) strategy was used to screen the optimal subset of biomarkers and construct nine machine-learning models based on this subset; the average accuracy, precision, recall, and F1-score were used for evaluation of the models. We identified 430 patients with sepsis and 686 patients with RTI. A total of 39 features were collected, with 23 features identified for initial model construction. Using the RFECV algorithm, we found that the XGBoost classifier, which only needed to include seven biomarkers, demonstrated the best performance among all prediction models, with an average accuracy of 89.24 ± 2.28, while the Ridge classifier, which included 11 biomarkers, had an average accuracy of only 83.87 ± 4.69. The remaining models had prediction accuracies greater than 88%. We developed nine models for predicting sepsis using a strategy that combined RFECV with machine learning. Among these models, the XGBoost classifier, which included seven biomarkers, showed the best performance and highest accuracy for predicting sepsis and may be a promising tool for the timely identification of sepsis.

Mitochondrial DNA Analysis of Teeth for Identification of Natural Disaster Victims in Manila, Philippines

Introduction: Natural disasters such as earthquakes, tsunamis and hurricanes often cause significant loss of life and damage to infrastructure. Identification of victims of natural disasters is an important aspect in the recovery process and forensic investigation. Mitochondrial DNA (mtDNA) has been proven to be an effective tool for individual identification due to its stable and maternally inherited nature. Teeth are an ideal source of DNA material for mtDNA analysis because they are durable against environmental degradation. This study aims to evaluate the effectiveness of mtDNA analysis of teeth for identifying victims of natural disasters in Manila, Philippines. Methods: Dental samples were collected from identified natural disaster victims and their family members. Mitochondrial DNA was extracted from teeth and analyzed using PCR and DNA sequencing techniques. The results of the mtDNA analysis were compared with mtDNA data obtained from family members to confirm the victim's identity. Results: Analysis of mtDNA on teeth succeeded in identifying all victims of natural disasters identified in this study. The mtDNA analysis results were consistent with mtDNA data obtained from family members, indicating the effectiveness of this method for individual identification. Conclusion: Analysis of mtDNA in teeth is a valuable tool for the identification of victims of natural disasters. This method can help in the recovery process and forensic investigations after natural disasters.

Role of Personalized Medicine in Myocardial Infarction With Nonobstructive Coronary Artery Disease (MINOCA): An Updated Review

Cardiovascular diseases, notably myocardial infarction (MI), contribute significantly to global mortality. While traditional MIs are often associated with notable coronary artery blockages, a subgroup experiences MI without such blockages, termed myocardial infarction with nonobstructive coronary arteries (MINOCA). Current diagnostic methods, like invasive coronary angiograms, are inadequate in diagnosing MINOCA due to the absence of visible vessel blockages. Personalized medicine endeavors to introduce novel diagnostic tools for early identification and tailored management of MINOCA. Unlike percutaneous coronary intervention, which lacks efficacy in MINOCA, personalized medical management aligned with the specific underlying pathophysiological cause is essential. Treatments may include statins, agents modulating the renin-angiotensin system, and antiplatelet therapies if linked to plaque disruption. At the same time, calcium channel blockers may be pivotal if coronary artery spasm is the primary cause. Research is ongoing to refine tailored therapies and discover new biomarkers, highlighting the crucial need for individualized approaches in managing MINOCA.

Development and application of a TaqMan-probe-based multiplex real-time PCR assay for simultaneous detection of porcine circovirus 2, 3, and 4 in Guangdong province of China

Porcine circoviruses disease (PCVD), caused by porcine circovirus (PCVs), is an important swine disease characterized by porcine dermatitis, nephrotic syndrome and reproductive disorders in sows. However, diseases caused by PCV2, PCV3, or PCV4 are difficult to distinguish, so a simple, rapid, accurate and high-throughput diagnostic and identification method is urgently needed to differentiate these three types. In this study, specific primers and probes were designed based on the conserved region sequences of the Rep gene of PCV2, and the Cap gene of PCV3 and PCV4. A multiplex qPCR assay was developed and optimized that the limit of detection concentration could reach as low as 3.8 copies/μL, with all correlation coefficients (R2) exceeding 0.999. Furthermore, the method showed no cross-reaction with other crucial porcine viral pathogens, and both intra-repeatability and inter-reproducibility coefficients of variation were below 2%. The assay was applied to the detection of 738 pig samples collected from 2020 to 2021 in Guangdong Province, China. This revealed positive infection rates of 65.18% for PCV2, 29.27% for PCV3, and 0% for PCV4, with a PCV2/PCV3 co-infection rate of 23.17%. Subsequently, complete genome sequences of 17 PCV2 and 4 PCV3 strains were obtained from the above positive samples and pre-preserved positive circovirus samples. Nucleotide sequence analysis revealed that the 17 PCV2 strains shared 96.7–100% complete nucleotide identity, with 6 strains being PCV2b and 11 strains being PCV2d; the 4 PCV3 strains shared 98.9–99.4% complete nucleotide identity, with 2 strains being PCV3a-1 and 2 strains being PCV3b. This research provides a reliable tool for rapid PCVs identification and detection. Molecular epidemiological investigation of PCVs in pigs in Guangdong Province will help us to understand PCV2 and PCV3 epidemiological characteristics and evolutionary trends.

Cumulative mortalities in white leg shrimp, Litopenaeus vannamei Boone 1931, cultured in biofloc system in Egypt reflected new record of Fusarium verticillioides infection

AbstractThe Egyptian aquaculture sector is still struggling to establish L. vannamei culture. In an effort to stimulate shrimp production with minimal environmental impact and less water loss, biofloc technology (BFT) has been employed. Chronic mortality was recently observed in L. vannamei which was raised in the BFT system. Examining samples of moribund shrimp revealed sporadic melanization on the musculature and gills, suggesting the potential existence of black gill disease. The present work was carried out to isolate and identify potentially implicated fungus species based on mycological properties of interest. To isolate and serve as initial identification tools, wet mount preparations and colonial features, along with slide culture techniques, were used. PCR assays, gene sequencing, and phylogenetic analyses were used in the molecular identification process to clarify the confirmation of the fungal isolates that were acquired. The pathogenicity tests were designed to fulfil Koch’s postulates in addition to clarifying the infectivity of isolated fungus. Moreover, histopathological examination was employed to notice tissues’ alterations associated with experimentally infected shrimp. The results revealed the isolation of three fungal isolates, namely, BNS 1117, BNS 2117, and BNS 3117, that were confirmatively identified as Fusarium verticillioides, and their selected sequences were submitted to Genbank. Moreover, experimental infection using F. verticillioides selected isolate (BNS 31,117) at both high (5 × 105 CFU/mL, group A) and low (3 × 105 CFU/mL, group B) doses of its conidial suspensions experienced cumulative mortality of 72.2% and 21.1%, respectively. Besides, the selected isolate was re-isolated from the experimentally infected shrimp in a pure form, which satisfied Koch’s postulates. Histopathological alterations demonstrated several degrees of either degenerative, oedema, or proliferative changes in the hepatopancreas, musculatures, and gills, respectively. This study sets a new record for F. verticillioides to be included in Fusarium species linked to shrimp mortality.

GMOIT: a tool for effective screening of genetically modified crops

BackgroundAdvancement in agricultural biotechnology has resulted in increasing numbers of commercial varieties of genetically modified (GM) crops worldwide. Though several databases on GM crops are available, these databases generally focus on collecting and providing information on transgenic crops rather than on screening strategies. To overcome this, we constructed a novel tool named, Genetically Modified Organisms Identification Tool (GMOIT), designed to integrate basic and genetic information on genetic modification events and detection methods.ResultsAt present, data for each element from 118 independent genetic modification events in soybean, maize, canola, and rice were included in the database. Particularly, GMOIT allows users to customize assay ranges and thus obtain the corresponding optimized screening strategies using common elements or specific locations as the detection targets with high flexibility. Using the 118 genetic modification events currently included in GMOIT as the range and algorithm selection results, a “6 + 4” protocol (six exogenous elements and four endogenous reference genes as the detection targets) covering 108 events for the four crops was established. Plasmids pGMOIT-1 and pGMOIT-2 were constructed as positive controls or calibrators in qualitative and quantitative transgene detection.ConclusionsOur study provides a simple, practical tool for selecting, detecting, and screening strategies for a sustainable and efficient application of genetic modification.

Molecular rapid diagnostic testing for bloodstream infections: Nanopore targeted sequencing with pathogen-specific primers

Nanopore sequencing, known for real-time analysis, shows promise for rapid clinical infection diagnosis but lacks effective assays for bloodstream infections (BSIs). We prospectively assessed the performance of a novel nanopore targeted sequencing (NTS) assay in identifying pathogens and predicting antibiotic resistance in BSIs, analyzing 387 blood samples from December 2021 to April 2023. The positivity rate for NTS (69.5%, 269/387) nearly matches that of metagenomic next-generation sequencing (mNGS) (74.7%, 289/387; p=0.128) and surpasses the positivity rate of conventional blood culture (BC) (33.9%, 131/387; p<0.01). Frequent pathogens detected by NTS included Klebsiella pneumoniae (n=54), Pseudomonas aeruginosa (n=36), Escherichia coli (n=36), Enterococcus faecium(n=30), Acinetobacter baumannii(n=26), Staphylococcus aureus(n=23), and Human cytomegalovirus (n=37). Against a composite BSI diagnostic standard, NTS demonstrated a sensitivity and specificity of 84.0% (95% CI 79.5%-87.7%) and 90.1% (95% CI 81.7%-88.5%), respectively. The concordance between NTS and mNGS results (the percentage of total cases where both either detected BSI-related pathogens or were both negative) was 90.2% (359/387), whereas the consistency between NTS and BC was only 60.2% (233/387). In 80.6% (50/62) of the samples with identical pathogens identified by both NTS tests and BCs, the genotypic resistance identified by NTS correlated with culture-confirmed phenotypic resistance. Using NTS, 95% of samples can be tested and analyzed in approximately 7hours, allowing for early patient diagnosis. NTS is rapid, sensitive, and efficient for detecting BSIs and drug-resistant genes, making it a potential preferred diagnostic tool for early infection identification in critically ill patients.

Relationship between gingival and peri-implant sulcular fluid active matrix metalloproteinase-8 concentration and clinical indices in healthy and diseased conditions

Aim: The study was to evaluate the active matrix metalloproteinase-8 (aMMP-8) concentration in gingival crevicular fluid (GCF) and in peri-implant sulcular fluid (PISF) in healthy and diseased conditions, before and after non-surgical treatment, and to compare it with the various clinical parameters used to estimate the gingival and peri-implant inflammation. Methods: Plaque index/modified PI (PI/mPI), gingival index/simplified GI (GI/sGI), probing depth (PD), bleeding on probing index/modified BOPI (BOPI/mBOPI), radiographic bone loss/radiographic marginal bone loss (rBL/rMBL), and GCF/PISF samples were evaluated, before and 3 months after non-surgical treatment, GCF/PISF samples were analyzed by a chair-side mouth-rinse test (ImplantSafe®) in combination with a digital reader (ORALyzer®). Results: In all groups, aMMP-8 median levels were statistically higher in the PISF than in GCF and they did not change after treatment. Moreover, it was statistically higher in Group 3 (periodontitis/peri-implantitis) compared to the other groups. A positive correlation of the GCF/PISF and aMMP-8 median concentration was seen with increasing PD and BOPI/mBOPI values. A higher covariation of aMMP-8 mean levels in GCF with PD was found when compared to PISF levels. aMMP-8 mean levels in PISF expressed a higher covariation with increasing grades of sGI, rMBL, and BOPI while aMMP-8 GCF concentration established a better covariation with PD and PI. Conclusions: PISF of sites with peri-implant mucositis and peri-implantitis showed higher levels of aMMP-8 compared to sites with gingivitis and periodontitis. Compared to clinical indices, aMMP-8 concentration in GCF/PISF can be a beneficial adjunctive diagnostic tool for early identification and screening of the risk of peri-implant diseases. After non-surgical therapy, PISF aMMP-8 concentration remained mostly unchanged, while the GCF concentration of aMMP-8 significantly decreased.

Risk prediction model based on machine learning for predicting miscarriage among pregnant patients with immune abnormalities.

Introduction: It is known that patients with immune-abnormal co-pregnancies are at a higher risk of adverse pregnancy outcomes. Traditional pregnancy risk management systems have poor prediction abilities for adverse pregnancy outcomes in such patients, with many limitations in clinical application. In this study, we will use machine learning to screen high-risk factors for miscarriage and develop a miscarriage risk prediction model for patients with immune-abnormal pregnancies. This model aims to provide an adjunctive tool for the clinical identification of patients at high risk of miscarriage and to allow for active intervention to reduce adverse pregnancy outcomes. Methods: Patients with immune-abnormal pregnancies attending Sichuan Provincial People's Hospital were collected through electronic medical records (EMR). The data were divided into a training set and a test set in an 8:2 ratio. Comparisons were made to evaluate the performance of traditional pregnancy risk assessment tools for clinical applications. This analysis involved assessing the cost-benefit of clinical treatment, evaluating the model's performance, and determining its economic value. Data sampling methods, feature screening, and machine learning algorithms were utilized to develop predictive models. These models were internally validated using 10-fold cross-validation for the training set and externally validated using bootstrapping for the test set. Model performance was assessed by the area under the characteristic curve (AUC). Based on the best parameters, a predictive model for miscarriage risk was developed, and the SHapley additive expansion (SHAP) method was used to assess the best model feature contribution. Results: A total of 565 patients were included in this study on machine learning-based models for predicting the risk of miscarriage in patients with immune-abnormal pregnancies. Twenty-eight risk warning models were developed, and the predictive model constructed using XGBoost demonstrated the best performance with an AUC of 0.9209. The SHAP analysis of the best model highlighted the total number of medications, as well as the use of aspirin and low molecular weight heparin, as significant influencing factors. The implementation of the pregnancy risk scoring rules resulted in accuracy, precision, and F1 scores of 0.3009, 0.1663, and 0.2852, respectively. The economic evaluation showed a saving of ¥7,485,865.7 due to the model. Conclusion: The predictive model developed in this study performed well in estimating the risk of miscarriage in patients with immune-abnormal pregnancies. The findings of the model interpretation identified the total number of medications and the use of other medications during pregnancy as key factors in the early warning model for miscarriage risk. This provides an important basis for early risk assessment and intervention in immune-abnormal pregnancies. The predictive model developed in this study demonstrated better risk prediction performance than the Pregnancy Risk Management System (PRMS) and also demonstrated economic value. Therefore, miscarriage risk prediction in patients with immune-abnormal pregnancies may be the most cost-effective management method.

Fast and accurate variant identification tool for sequencing-based studies

BackgroundAccurate identification of genetic variants, such as point mutations and insertions/deletions (indels), is crucial for various genetic studies into epidemic tracking, population genetics, and disease diagnosis. Genetic studies into microbiomes often require processing numerous sequencing datasets, necessitating variant identifiers with high speed, accuracy, and robustness.ResultsWe present QuickVariants, a bioinformatics tool that effectively summarizes variant information from read alignments and identifies variants. When tested on diverse bacterial sequencing data, QuickVariants demonstrates a ninefold higher median speed than bcftools, a widely used variant identifier, with higher accuracy in identifying both point mutations and indels. This accuracy extends to variant identification in virus samples, including SARS-CoV-2, particularly with significantly fewer false negative indels than bcftools. The high accuracy of QuickVariants is further demonstrated by its detection of a greater number of Omicron-specific indels (5 versus 0) and point mutations (61 versus 48–54) than bcftools in sewage metagenomes predominated by Omicron variants. Much of the reduced accuracy of bcftools was attributable to its misinterpretation of indels, often producing false negative indels and false positive point mutations at the same locations.ConclusionsWe introduce QuickVariants, a fast, accurate, and robust bioinformatics tool designed for identifying genetic variants for microbial studies. QuickVariants is available at https://github.com/caozhichongchong/QuickVariants.