Routine Diagnostic Tool Research Articles

Automatic identification of clinically important Aspergillus species by artificial intelligence-based image recognition: proof-of-concept study.

While morphological examination is the most widely used for Aspergillus identification in clinical laboratories, PCR-sequencing and MALDI-TOF MS are emerging technologies in more financially-competent laboratories. However, mycological expertise, molecular biologists and/or expensive equipment are needed for these. Recently, artificial intelligence (AI), especially image recognition, is being increasingly employed in medicine for fast and automated disease diagnosis. We explored the potential utility of AI in identifying Aspergillus species. In this proof-of-concept study, using 2,813, 2,814 and 1,240 images from four clinically important Aspergillus species for training, validation and testing, respectively; the performances and accuracies of automatic Aspergillus identification using colonial images by three different convolutional neural networks were evaluated. Results demonstrated that ResNet-18 outperformed Inception-v3 and DenseNet-121 and is the best algorithm of choice because it made the fewest misidentifications (n = 8) and possessed the highest testing accuracy (99.35%). Images showing more unique morphological features were more accurately identified. AI-based image recognition using colonial images is a promising technology for Aspergillus identification. Given its short turn-around-time, minimal demand of expertise, low reagent/equipment costs and user-friendliness, it has the potential to serve as a routine laboratory diagnostic tool after the database is further expanded.

Enhancing pathogens detection in suspected geriatric bloodstream infections using Nanopore-targeted sequencing.

Bloodstream infections (BSIs) in elderly patients pose substantial diagnostic and therapeutic challenges due to the limitations of traditional blood culture methods, which are hampered by slow turnaround times and false-negatives. Nanopore-targeted sequencing (NTS) emerges as a significant advancement, offering rapid and accurate pathogen detection directly from blood samples. This study demonstrates that NTS provides a higher detection rate and faster results than conventional blood cultures, crucial for the timely management of BSIs in geriatric patients, who often present with multiple or mixed infections and have poorer clinical outcomes. The findings underscore the potential of NTS to enhance diagnostic accuracy and speed, informing more effective treatment strategies and improving overall patient outcomes. Further research is essential to establish NTS as a routine diagnostic tool in the clinical management of BSIs in the elderly.

The study on ultrasound image classification using a dual-branch model based on Resnet50 guided by U-net segmentation results

In recent years, the incidence of nodular thyroid diseases has been increasing annually. Ultrasonography has become a routine diagnostic tool for thyroid nodules due to its high real-time capabilities and low invasiveness. However, thyroid images obtained from current ultrasound tests often have low resolution and are plagued by significant noise interference. Regional differences in medical conditions and varying levels of physician experience can impact the accuracy and efficiency of diagnostic results. With the advancement of deep learning technology, deep learning models are used to identify whether a nodule in a thyroid ultrasound image is benign or malignant. This helps to close the gap between doctors’ experience and equipment differences, improving the accuracy of the initial diagnosis of thyroid nodules. To cope with the problem that thyroid ultrasound images contain complex background and noise as well as poorly defined local features. in this paper, we first construct an improved ResNet50 classification model that uses a two-branch input and incorporates a global attention lightening module. This model is used to improve the accuracy of benign and malignant nodule classification in thyroid ultrasound images and to reduce the computational effort due to the two-branch structure.We constructed a U-net segmentation model incorporating our proposed ACR module, which uses hollow convolution with different dilation rates to capture multi-scale contextual information for feature extraction of nodules in thyroid ultrasound images and uses the results of the segmentation task as an auxiliary branch for the classification task to guide the classification model to focus on the lesion region more efficiently in the case of weak local features. The classification model is guided to focus on the lesion region more efficiently, and the classification and segmentation sub-networks are respectively improved specifically for this study, which is used to improve the accuracy of classifying the benign and malignant nature of the nodules in thyroid ultrasound images. The experimental results show that the four evaluation metrics of accuracy, precision, recall, and f1 of the improved model are 96.01%, 93.3%, 98.8%, and 96.0%, respectively. The improvements were 5.7%, 1.6%, 13.1%, and 7.4%, respectively, compared with the baseline classification model.

A Flow Cytometry-Based Examination of the Mouse White Blood Cell Differential in the Context of Age and Sex.

Analysis of the white blood cell differential as part of a flow cytometry-based approach is a common routine diagnostic tool used in clinics and research. For human blood, the methodological approach, suitable markers, and gating strategies are well-established. However, there is a lack of information regarding the mouse blood count. In this article, we deliver a fast and easy protocol for reprocessing mouse blood for the purpose of flow cytometric analysis, as well as suitable markers and gating strategies. We also present two possible applications: for the analysis of the whole blood count, with blood from a cardiac puncture, and for the analysis of a certain leukocyte subset at multiple time points in the framework of a mouse experiment, using blood from the facial vein. Additionally, we provide orientation values by applying the method to 3-month-old and 24-month-old male and female C57BL/6J mice. Our analyses demonstrate differences in the leukocyte fractions depending on age and sex. We discuss the influencing factors and limitations that can affect the results and that, therefore, need to be considered when applying this method. The present study fills the gap in the knowledge related to the rare information on flow cytometric analysis of mouse blood and, thus, lays the foundation for further investigations in this area.

Gut Microbiota-Related Biomarkers in Immuno-Oncology.

Carcinogenesis is associated with the emergence of protracted intestinal dysbiosis and metabolic changes. Increasing evidence shows that gut microbiota-related biomarkers and microbiota-centered interventions are promising strategies to overcome resistance to immunotherapy. However, current standard methods for evaluating gut microbiota composition are cost- and time-consuming. The development of routine diagnostic tools for intestinal barrier alterations and dysbiosis constitutes a critical unmet medical need that can guide routine treatment and microbiota-centered intervention decisions in patients with cancer. In this review, we explore the influence of gut microbiota on cancer immunotherapy and highlight gut-associated biomarkers that have the potential to be transformed into simple diagnostic tools, thus guiding standard treatment decisions in the field of immuno-oncology. Mechanistic insights toward leveraging the complex relationship between cancer immunosurveillance, gut microbiota, and metabolism open exciting opportunities for developing novel biomarkers in immuno-oncology.

Can the SIARI score be used at initial admission to predict testes involvement in patients with fournier’s gangrene; a single-centre, retrospective study

BackgroundTo predict testicular involvement in patients diagnosed with Fournier’s gangrene (FG) using the Laboratory Risk Indicator for Necrotizing Fasciitis (LRINEC) score and the site other than lower limb (SIARI) score.MethodsThe medical records of 51 patients operated for FG in our clinic between December 2012 and April 2022 were evaluated retrospectively in this study. Patients’ demographics, and laboratory test results were compared with the testisticular involvement status. Patients with testisticular involvement (n = 10) were compared with patients without testicular involvement (n = 41). The SIARI score at initial admission was analysed using logistic regression analyses for its performance in predicting testicular involvement with FG. Receiver operating characteristics (ROC) curves and the area under the receiver operating characteristic curve (AUROC) were used to evaluate its discriminating ability.ResultsThe SIARI score had modest performance for diagnosing testicular involvement in FG patients, with ROC analysis showing an AUROC value of 0.83 (p < 0.001). With a SIARI cut-off score of ≥ 3, the sensitivity was 90% and the specificity was 68%. For a SIARI cut-off score of ≥ 5, the sensitivity was 40% and the specificity was 97%.ConclusionsThe ability of the SIARI score to discriminate FG with testicular involvement is modest. The SIARI score should be employed cautiously as a routine diagnostic tool for the prediction of testicular involvement in FG at the initial admission. More research is needed to develop a better understanding of the relationship between the SIARI score and testicular involvement in FG.

Hypomania-Checklist-33: risk stratification and factor structure in a mixed psychiatric adolescent sample

BackgroundThe 33-item Hypomania Checklist (HCL-33) has been shown to distinguish between adolescent bipolar disorder (BD) and unipolar depression. To investigate the utility of the HCL-33 as a screening tool in routine diagnostics, the frequency and psychopathological characteristics of detected individuals in a mixed psychiatric sample necessitate more examination.MethodsThe HCL-33, Children’s Depression Inventory, Beck’s Anxiety Inventory, and Strengths and Difficulties Questionnaire were completed by 285 children and adolescents (12–18 years) in a mixed psychiatric sample. Applying the proposed HCL-33 cut-off score of ≥ 18, individuals with depressive symptoms were divided into at-risk or not at-risk for BD groups. The factorial structure, sum and factor score correlations with psychopathology, and impact on daily functioning were assessed.Results20.6% of the sample met at-risk criteria for BD. These individuals (n = 55) were older, more anxious, and showed more conduct problems vs the not at-risk group (n = 107). A two- and a three-factor model were pursued with the same Factor 1 (“active-elated”). Factor 2 (“risk-taking/irritable”) was separated into 2a (“irritable-erratic”) and 2b (“outgoing-disinhibited”) in the three-factor model. Whereas higher Factor 2 and 2a scores correlated with a broad range of more severe symptomatology (i.e., depression, anxiety, hyperactivity), higher Factor 1 and 2b scores correlated with more emotional and conduct problems, respectively. 51.7% of the sample reported a negative impact from hypomanic symptoms on daily functioning.LimitationsCross-sectional design and data collection in a single mental health service.ConclusionsThe HCL-33 may be a useful tool to improve diagnostics, especially in adolescents with depressive symptoms additionally presenting with anxious symptoms and conduct problems.

Root resorption of maxillary lateral incisors associated with maxillary impacted canines: Cone-beam computed tomography and panoramic radiography study

: The position of maxillary impacted canine (MIC) and the presence of lateral incisor root resorption (RR) remains a challenge for interceptive orthodontics and surgical treatment planning. The use of panoramic radiographs will be compared with CBCT in the diagnosis of RR and emphasize the importance of accurate diagnostic imaging.: To compare the diagnostic accuracy of Cone Beam Computed Tomography and Panoramic Radiography in detecting lateral incisor RR associated with MIC. To evaluate, with respect to sector position of impacted canines, the agreement between panoramic radiographs and CBCT in detecting lateral incisor RR.: A cross-sectional retrospective descriptive study on panoramic and CBCT radiographic records between January 2014 and December 2020. The study setting was Section of Diagnostic Imaging at a University Oral Health Centre. All patients requiring radiographs are referred to this section by all dental departments of the dental hospital. The study sample consisted of CBCT and panoramic images of impacted maxillary canines of patients older than 9yrs. Patients with odontogenic and non-odontogenic pathology in the canine and incisor area, those with previous diagnosis of external root resorption verified from the patient’s treatment records, and patients who have had any form of orthodontic treatment; removable or fixed appliances were excluded. Data was analysed using Statistical Package for the Social Sciences (SPSS) version 27. Pearson's chi-squared test was used to analyse the agreement between panoramic radiographs and CBCT in locating canine sector position. All tests were conducted at 5% significance level. Fifty patients records were analysed in our study. The median age of the sample was 15 years and the IQR was 12.5-19. Our sample consisted predominantly of females (n=40) compared to males(n=10). A total of 65 MIC were assessed with 16 bilateral impactions (n=32) and the unilateral impactions (n=33). Of the 65 MICs, 46% were located on the right side and 54% were located on the left side. The CBCT examination showed that 58% (n = 38) of MIC were located palatally, 34% (n=22) labially and 8% (n=5) in the mid-alveolus (between the teeth). The majority of MIC occurred in sector 4 in 2-D pan (26%, n=17) and 3-D pan at (32%, n=21). Pearson's chi-squared test results showed a statistically significant difference in the agreement between 2-D pan and 3-D pan in locating canine sector position (p=0.001).: The detection of root resorption in our study underscores the importance for clinicians to examine panoramic radiographs thoroughly beyond the counting of teeth. Although panoramic radiographs are the routine diagnostic tool in contemporary dental practice, their limitation in definitive diagnosis of root resorption should be augmented with the use of CBCT.

Diagnostic accuracy of high resolution computed tomography (HRCT) of chest in diagnosing sputum smear positive and sputum smear negative pulmonary tuberculosis (PTB).

Objective: To assess the diagnostic accuracy of high resolution computed tomography (HRCT) of chest in diagnosing sputum smear positive and negative pulmonary tuberculosis (PTB). Study Design: Cross-sectional study. Setting: Tertiary Care Hospital, Kharian Cantt. Period: Oct 2021 to April 2022. Methods: The retrospective reviews of medical records of patients undergone HRCT chest and sputum for AFB smear and culture due to pulmonary tuberculosis (PTB) suspicion were included. The HRCT and AFB findings for sample size of 220 was compared. SPSS was used to stratify outcomes on basis of sputum smear results. Diagnostic accuracy of HRCT chest was calculated by taking AFB as gold standard. The AFB findings and HRCT outcomes were compared through correlation analysis and paired t-test with p value &lt; 0.05 considered as significant. Results: The sensitivity was found to be 86.23% with specificity of 86.48%, positive predictive value of 86.23%, negative predictive value of 86.48% and diagnostic accuracy of 86.36% for HRCT for diagnosing pulmonary tuberculosis (PTB). Conclusion: HRCT chest affirms high diagnostic potential for pulmonary tuberculosis (PTB) and should be used as a routine diagnostic tool for the disease.

GenoMycAnalyzer: a web-based tool for species and drug resistance prediction for Mycobacterium genomes

BackgroundDrug-resistant tuberculosis (TB) is a major threat to global public health. Whole-genome sequencing (WGS) is a useful tool for species identification and drug resistance prediction, and many clinical laboratories are transitioning to WGS as a routine diagnostic tool. However, user-friendly and high-confidence automated bioinformatics tools are needed to rapidly identify M. tuberculosis complex (MTBC) and non-tuberculous mycobacteria (NTM), detect drug resistance, and further guide treatment options.ResultsWe developed GenoMycAnalyzer, a web-based software that integrates functions for identifying MTBC and NTM species, lineage and spoligotype prediction, variant calling, annotation, drug-resistance determination, and data visualization. The accuracy of GenoMycAnalyzer for genotypic drug susceptibility testing (gDST) was evaluated using 5,473 MTBC isolates that underwent phenotypic DST (pDST). The GenoMycAnalyzer database was built to predict the gDST for 15 antituberculosis drugs using the World Health Organization mutational catalogue. Compared to pDST, the sensitivity of drug susceptibilities by the GenoMycAnalyzer for first-line drugs ranged from 95.9% for rifampicin (95% CI 94.8–96.7%) to 79.6% for pyrazinamide (95% CI 76.9–82.2%), whereas those for second-line drugs ranged from 98.2% for levofloxacin (95% CI 90.1–100.0%) to 74.9% for capreomycin (95% CI 69.3–80.0%). Notably, the integration of large deletions of the four resistance-conferring genes increased gDST sensitivity. The specificity of drug susceptibilities by the GenoMycAnalyzer ranged from 98.7% for amikacin (95% CI 97.8–99.3%) to 79.5% for ethionamide (95% CI 76.4–82.3%). The incorporated Kraken2 software identified 1,284 mycobacterial species with an accuracy of 98.8%. GenoMycAnalyzer also perfectly predicted lineages for 1,935 MTBC and spoligotypes for 54 MTBC.ConclusionsGenoMycAnalyzer offers both web-based and graphical user interfaces, which can help biologists with limited access to high-performance computing systems or limited bioinformatics skills. By streamlining the interpretation of WGS data, the GenoMycAnalyzer has the potential to significantly impact TB management and contribute to global efforts to combat this infectious disease. GenoMycAnalyzer is available at http://www.mycochase.org.

Artificial intelligence in routine blood tests

Routine blood tests drive diagnosis, prognosis, and monitoring in traditional clinical decision support systems. As a routine diagnostic tool with standardized laboratory workflows, clinical blood analysis offers superior accessibility to a comprehensive assessment of physiological parameters. These parameters can be integrated and automated at scale, allowing for in-depth clinical inference and cost-effectiveness compared to other modalities such as imaging, genetic testing, or histopathology. Herein, we extensively review the analytical value of routine blood tests leveraged by artificial intelligence (AI), using the ICD-10 classification as a reference. A significant gap exists between standard disease-associated features and those selected by machine learning models. This suggests an amount of non-perceived information in traditional decision support systems that AI could leverage with improved performance metrics. Nonetheless, AI-derived support for clinical decisions must still be harmonized regarding external validation studies, regulatory approvals, and clinical deployment strategies. Still, as we discuss, the path is drawn for the future application of scalable artificial intelligence (AI) to enhance, extract, and classify patterns potentially correlated with pathological states with restricted limitations in terms of bias and representativeness.

A multicenter clinical AI system study for detection and diagnosis of focal liver lesions

Early and accurate diagnosis of focal liver lesions is crucial for effective treatment and prognosis. We developed and validated a fully automated diagnostic system named Liver Artificial Intelligence Diagnosis System (LiAIDS) based on a diverse sample of 12,610 patients from 18 hospitals, both retrospectively and prospectively. In this study, LiAIDS achieved an F1-score of 0.940 for benign and 0.692 for malignant lesions, outperforming junior radiologists (benign: 0.830-0.890, malignant: 0.230-0.360) and being on par with senior radiologists (benign: 0.920-0.950, malignant: 0.550-0.650). Furthermore, with the assistance of LiAIDS, the diagnostic accuracy of all radiologists improved. For benign and malignant lesions, junior radiologists’ F1-scores improved to 0.936-0.946 and 0.667-0.680 respectively, while seniors improved to 0.950-0.961 and 0.679-0.753. Additionally, in a triage study of 13,192 consecutive patients, LiAIDS automatically classified 76.46% of patients as low risk with a high NPV of 99.0%. The evidence suggests that LiAIDS can serve as a routine diagnostic tool and enhance the diagnostic capabilities of radiologists for liver lesions.

Investigating Cytogenetic Profiles in Couples Experiencing Recurrent Implantation Failure Post&lt;i&gt; in vitro&lt;/i&gt; Fertilization

This study evaluates how chromosomal factors affect assisted reproduction techniques (ART) challenges among infertile couples, impacting their chances of conception. Chromosomal abnormalities, a leading cause of pregnancy failure and miscarriages, were investigated in a four-year retrospective study involving 100 patients with a history of infertility and unsuccessful IVF treatment. Among these cases, nine (9%) displayed aberrant chromosomal patterns, including balanced translocations (5%), sex chromosome deletions (3%), and one case of a small supernumerary marker chromosome (sSMC) (1%). The results of the present study highlight the importance of integrating comprehensive cytogenetic analysis as a routine diagnostic tool for individuals dealing with infertility, particularly before assisted reproduction techniques, to avert recurrent implantation failure and to enhance their chances of success.

Diagnostic Tool for the Identification of Bactrocera dorsalis (Hendel) (Diptera: Tephritidae) Using Real-Time PCR.

Accurate identification of Bactrocera dorsalis (Hendel) (Diptera: Tephritidae), commonly known as the Oriental fruit fly, is a significant challenge due to the morphological convergence and taxonomic uncertainties of species belonging to the same genus. This highly polyphagous species poses a significant threat to fruit crops. With its potential establishment in Europe becoming a growing concern, there is an urgent need for rapid and efficient diagnostic methods. The study presented here introduces a diagnostic protocol based on real-time PCR using a TaqMan probe for the early and reproducible identification of B. dorsalis. Specimens representing the genetic diversity of the Italian population were collected and analyzed. Specific primers and probe were designed based on the conserved regions and an in silico analysis confirmed their specificity. The assay conditions were optimized, and analytical sensitivity, specificity, repeatability, and reproducibility were evaluated. The protocol showed high sensitivity and specificity, accurately detecting low DNA concentrations of B. dorsalis. This standardized method provides a reliable tool for routine diagnostics, enhancing the accuracy and efficiency of identifying the Oriental fruit fly at all stages of its development, thereby facilitating effective pest management measures. The development of this diagnostic protocol is crucial for monitoring and supporting efforts to prevent the passive spread of B. dorsalis in Europe, particularly in light of the recent active infestations detected in Italy.

Artificial Intelligence-Enabled Prostate Cancer Diagnosis and Prognosis: Current State and Future Implications.

In this modern era of digital pathology, artificial intelligence (AI)-based diagnostics for prostate cancer has become a hot topic. Multiple retrospective studies have demonstrated the benefits of AI-based diagnostic solutions for prostate cancer that includes improved prostate cancer detection, quantification, grading, interobserver concordance, cost and time savings, and a potential to reduce pathologists' workload and enhance pathology laboratory workflow. One of the major milestones is the Food and Drug Administration approval of Paige prostate AI for a second review of prostate cancer diagnosed using core needle biopsies. However, implementation of these AI tools for routine prostate cancer diagnostics is still lacking. Some of the limiting factors include costly digital pathology workflow, lack of regulatory guidelines for deployment of AI, and lack of prospective studies demonstrating the actual benefits of AI algorithms. Apart from diagnosis, AI algorithms have the potential to uncover novel insights into understanding the biology of prostate cancer and enable better risk stratification, and prognostication. This article includes an in-depth review of the current state of AI for prostate cancer diagnosis and highlights the future prospects of AI in prostate pathology for improved patient care.

Pioneering predictions of AKI and AKIN severity in burn patients: a comprehensive CBC approach

This study aims to evaluate the utility of complete blood count (CBC) markers, in conjunction with the acute kidney injury network (AKIN) criteria, for the early detection, severity assessment, and prediction of mortality outcomes of acute kidney injury (AKI) in burn patients. The research seeks to fill existing gaps in knowledge and validate the cost-effectiveness of using CBC as a routine diagnostic tool for better management of AKI. The study was conducted at Hangang Sacred Heart Hospital. We performed a large-scale retrospective analysis of 2758 adult patients admitted to the burn intensive care unit over a 12-year period. Among these patients, AKI occurred in 1554 patients (56.3%). Based on the AKIN stage classification, 794 patients (28.8%) were categorized as AKIN 1, 494 patients (17.9%) as AKIN 2, and 266 patients (9.6%) as AKIN 3. We defined several ratio markers, including the Neutrophil-to-lymphocyte ratio (NLR), Platelet-to-lymphocyte ratio (PLR), Monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), and various mean platelet volume (MPV) ratios. Our statistical analyses, conducted using the R programming language, revealed significant correlations between these markers and AKI severity. The AUC values for neutrophil count and WBC count were 0.790 and 0.793, respectively, followed by immature granulocyte count with an AUC of 0.727. For red blood cell (RBC)-related parameters, the AUC values for hematocrit (Hct), hemoglobin (Hb), and RBC count were 0.725, 0.713, and 0.713, respectively. Among the platelet-related parameters, only platelet distribution width (PDW) had an AUC of 0.677. Among the ratio markers, the NLR had the highest AUC at 0.772, followed by MPVNR and SII with AUC values of 0.700 and 0.680, respectively. The findings underscore the potential of CBC as an economical, routine test for AKI, thereby paving the way for enhanced patient outcomes. Our study suggests the utility of routine CBC tests, specifically WBC count and PLR, for predicting AKI and platelet, MPV, and NLR for mortality assessment in burn patients. These findings underscore the potential of easily accessible CBC tests in enhancing AKI management. However, further multicenter studies are needed for validation.

A lightweight hybrid CNN-LSTM explainable model for ECG-based arrhythmia detection

Objective:Electrocardiogram (ECG) is the most frequent and routine diagnostic tool used for monitoring heart electrical signals and evaluating its functionality. The human heart can suffer from a variety of diseases, including cardiac arrhythmias. Arrhythmia is an irregular heart rhythm that in severe cases can lead to stroke and can be diagnosed via ECG recordings. Since early detection of cardiac arrhythmias is of great importance, computerized and automated classification and identification of these abnormal heart signals have received much attention for the past decades. Methods:This paper introduces a light Deep Learning (DL) approach for high accuracy detection of 8 different cardiac arrhythmias and normal rhythms. To employ DL techniques, the ECG signals were preprocessed using resampling and baseline wander removal techniques. The classification was performed using an 11-layer network employing a combination of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). Results:In order to evaluate the proposed technique, ECG signals are chosen from the two physionet databases, the MIT-BIH arrhythmia database and the long-term AF database. The proposed DL framework based on the combination of CNN and LSTM showed promising results than most of the state-of-the-art methods. The proposed method reaches the mean diagnostic accuracy of 98.24%. Conclusion:A trained model for arrhythmia classification using diverse ECG signals were successfully developed and tested. Significance:This study presents a lightweight classification technique with high diagnostic accuracy compared to other notable methods, making it a potential candidate for implementation in Holter monitor devices for arrhythmia detection. Finally, we used SHapley Additive exPlanations (SHAP), the most popular Explainable Artificial Intelligence (XAI) method to understand how our model make predictions. The results indicate that those features (ECG samples) that have contributed the most to a prediction are consonant with clinicians’ decisions. Therefore, the use of interpretable models increases the trust of clinicians in AI and thus leads to decreasing the number of misdiagnoses of cardiovascular diseases.

Profiling of the genetic features of patients with breast, ovarian, colorectal and extracolonic cancers: Association to CHEK2 and PALB2 germline mutations

Background and aimsCancer predisposition goes beyond BRCA and DNA Mismatch Repair (MMR) genes since multi-gene panel testing has become the routine diagnostic tool for hereditary cancer suspicion (HCS) cases. CHEK2 and PALB2 are some of the foremost-mutated non-BRCA/MMR actionable genes in families with a significant familial aggregation. Therefore, the purpose of this work is to unravel which tumours other than breast, ovary or colorectal display the patients. Materials and methodsWe have analysed 528 probands that meet the inclusion criteria for Hereditary Breast and Ovarian Cancer and Lynch Syndrome established by our Hereditary Cancer Regional Program with a customized 35 genes-panel by using Ion Torrent™ Technology. ResultsWe have identified pathogenic variants (PVs) in 61 families (1.55%), of which more than half (31 probands) harboured PVs in CHEK2 and PALB2 genes. Ours results reveal that not only were PVs CHEK2 and PALB2 carriers more likely to have family history of cancer not limited to breast, ovarian or colorectal cancers, but also they are prone to other extracolonic cancers, noteworthy endometrial and gastric cancers. ConclusionsMultigene panel testing improves the chance of finding PVs in actionable genes in families with HCS. In addition, the coexistence of variants should be recorded to implement a polygenic risk algorithm that might explain the missing heritability in the aforementioned families.

Unreliable diagnostic accuracy of laboratory risk indicator for necrotizing fasciitis (LRINEC) score but good outcome predictor in necrotizing fasciitis due to Vibrio vulnificus: A retrospective and matched-pair study

The diagnostic accuracy of laboratory risk indicator for necrotizing fasciitis (LRINEC) score system in specific Vibrio vulnificus (V vulnificus) necrotizing fasciitis (NF) have not been fully investigated yet. This aim of our study is to validate the LRINEC score in patients with V vulnificus NF. A retrospective study of hospitalized patients was conducted in a hospital in southern Taiwan between January 2015 and December 2022. Clinical characteristics, variables and outcomes were compared among V vulnificus NF, non-Vibrio NF and cellulitis patients. A total of 260 patients were included, 40 in V vulnificus NF group, 80 in non-Vibrio NF group and 160 patients in cellulitis group. In V vulnificus NF group with an LRINEC cutoff score ≥ 6, the sensitivity was 35% (95% confidence interval [CI]: 29%–41%), specificity was 81% (95% CI: 76%–86%), PPV was 23% (95% CI: 17%–27%), and NPV was 90% (95% CI: 88%–92%). The AUROC for accuracy of the LRINEC score in V vulnificus NF was 0.614 (95% CI: 0.592–0.636). Multi-variable logistic regression analysis revealed that LRINEC &gt; 8 was significantly associated with higher in-hospital mortality (adjusted odds ratio = 1.57; 95% CI: 1.43–2.08; P &lt; .01). The LRINEC score may not be an accurate tool for V vulnificus NF. That should be used with caution as a routine diagnostic tool. However, LRINEC &gt; 8 is significantly associated with higher mortality in V vulnificus NF patients.

Hinge abduction hip dysplasia in (morquio a syndrome) treated by proximal femoral valgization osteotomy: a rare case report.

A 10-year-old girl suffering from MPS IVA with many signs of orthopedic manifestations. When concentrating on the hip joint, she had acetabulofemoral dysplasia and hinge abduction hip with the aid of plain radiographs and arthrography with dynamic testing. A valgization osteotomy of the proximal femur in combination with a shelf acetabuloplasty was performed bilaterally. There is no documented case of valgus osteotomy of the proximal femur in MPS IVA patients. Furthermore, preoperative arthrographies are not described as a routine diagnostic tool, because the routine surgical procedure was to do varus osteotomy with a high rate of failure. In our opinion, understanding of the dynamic function of the hip is essential for the surgical decision-making process. Our successful case with an 8-year follow-up illustrates that the well-known and widely performed valgus osteotomy in cases of hinge abduction in MPS IVA is an alternative, which should be considered preoperatively.