Quick Screening Research Articles

Characterization of Archaea membrane lipids in radioactive springs using shotgun lipidomics.

Lipids from microorganisms, and especially lipids from Archaea, are used as taxonomic markers. Unfortunately, knowledge is very limited due to the uncultivability of most Archaea, which greatly reduces the importance of the diversity of lipids and their ecological role. One possible solution is to use lipidomic analysis. Six radioactive sources were investigated, two of which are surface (Wettinquelle and Radonka) and four deep from the Svornost mine (Agricola, Behounek, C1, and Curie). A total of 15 core lipids and 82 intact polar lipids were identified from the membranes of microorganisms in six radioactive springs. Using shotgun lipidomics, typical Archaea lipids were identified in spring water, namely dialkyl glycerol tetraethers, archaeol, hydroxyarchaeol and dihydroxyarchaeol. Diverse groups of polar heads were formed in archaeal IPLs, whose polar heads are formed mainly by hexose, deoxyhexose, and phosphoglycerol. The analysis was performed using shotgun lipidomics and the structure of all molecular species was confirmed by tandem mass spectrometry. After acid hydrolysis, a mixture of polar compounds was obtained from the polar head. Further analysis by GC-MS confirmed that the carbohydrates were glucose and rhamnose. Analysis by HPLC-MS of diastereoisomers of 2-(polyhydroxyalkyl)-3-(O-tolylthiocarbamoyl)thiazolidine-4(R)-carboxylates revealed that both L-rhamnose and D-glucose are present in spring samples only in varying amounts. The glycoside composition depends on the type of spring, that is, Wettinquelle and Radonka springs are basically shallow groundwater, while the samples from the Svornost mine are deep groundwater and do not contain glycosides with rhamnose. This method enables quick screening for characteristic Archaea lipids, allowing decisions on whether to pursue further analyses, such as metagenomic analysis, to directly confirm the presence of Archaea.

AI-driven design and optimization of nanoparticle-based drug delivery systems

Nanoparticle-based drug delivery systems represent a transformative advancement in targeted therapeutics, providing meticulous drug delivery, enhanced bioavailability, and diminished side effects. However, designing nanoparticles (NPs) optimal for specific drugs and diseases remains a complex challenge. The advancements in artificial intelligence (AI) have provided innovative approaches to design and optimize these systems, improving their efficacy and adaptability. This review encompasses the integration of AI in the conceptualization and development of NP drug delivery systems, signifying its potential to revolutionize the field. The review discusses the different AI methods such as machine learning, neural networks, and optimization algorithms that simplify the fabrication of NPs with tailored characteristics such as size, surface chemistry, and drug release profiles. AI can also standardize these characteristics to enhance drug loading capacity, targeting specificity, and controlled release at the chosen site of action. AI-based predictive modeling enables the quick screening of numerous parameters, thus quickening the discovery of optimal NP configurations tailored to specific therapeutic needs. Furthermore, the review also discusses the case studies where AI has efficaciously forecasted NP behavior in biological environments, crucial for enhanced targeting and diminished off-target effects. The amalgamation of AI and nanotechnology not only streamlines the drug development process but also paves the way for personalized medicine. The review also entails the different challenges associated with implementing AI in this field, such as data quality, algorithm transparency, and regulatory specifications. By utilizing AI, researchers and healthcare providers can unlock new potentials in novel drug delivery systems, ultimately advancing the precision and effectiveness of treatments for various diseases. Finally, the review discusses the future directions of AI-based NP design, highlighting its benefits to transform drug delivery and augment patient outcomes.

Prevalence of Dysphonia Among School Going Children in Dakshina Kannada District in Karnataka

ObjectiveVoice disorders among children are quite common, probably due to the excessive and phonotraumatic voice use patterns. Global prevalence falls between 1.4% and 53.2%; however, no such prevalence studies are available in Indian literature. This study aimed to estimate the prevalence of perceptual dysphonia among school going children in the Dakshina Kannada district in Karnataka, India. Study DesignCross sectional study. MethodA total of 3005 school going children aged between 6 and 17 were recruited through voice screening camps conducted across 16 schools through cluster sampling. They were screened using an auditory perception tool—consensus auditory perceptual evaluation of voice (CAPE-V) and adapted The Quick Screen for Voice protocol. ResultsThe prevalence of perceptual dysphonia was estimated to be 6.92% based on clinician rated CAPE-V ratings and The Quick Screen for Voice protocol. The measures of vocal tasks carried out as part of the protocol were grouped based on the age of the children. ConclusionThe estimated prevalence highlighted the need for future epidemiological studies. Findings from such epidemiological studies will enable early identification, assessment, and treatment planning for pediatric voice disorders.

Evaluation of the immune status of dogs vaccinated against rabies by an enzyme-linked immunosorbent assay using crude preparations of insect cells infected with a recombinant baculovirus encoding the rabies virus glycoprotein gene.

Evaluation of the effectiveness of vaccination of animals against rabies is not routinely implemented. In cases where it is carried out, the rapid fluorescent focus inhibition test (RFFIT) or the fluorescent antibody virus neutralization (FAVN) test are the recommended tests. However, both of these tests require handling of live rabies virus (RABV), and are cumbersome to perform. In view of this, the enzyme-linked immunosorbent assay (ELISA) has been proposed as a surrogate test; however, availability of appropriate antigen is a major impediment for the development of ELISAs to detect anti-rabies antibodies. The most widely used antigen is the RABV glycoprotein (G) purified from cell culture-propagated virus, which requires a biosafety level 3 containment. The alternative is to use recombinantly expressed G, which needs to be to be properly glycosylated and folded to serve as the best antigen. The most suitable system for its production is the baculovirus expression system (BVES). However, purification of RABV G is challenging. We therefore tested partially purified preparations in the form of extracts of insect cells infected with baculovirus expressing RABV G, against sera from vaccinated dogs in an indirect ELISA. The results showed good concordance against RFFIT, with sensitivity and specificity of 90.48% and 80.00%, respectively. The system may be used for quick screening to determine the presence and an approximate level of antibodies, and can be modified to enable monitoring of mass dog vaccination programs, as well as to facilitate certification of dogs intended for international travel and transportation.

Maintenance Priority: A Literature Review of Equipment Criticality Analysis in the Oil and Gas Industries

Abstract Criticality analysis is a fundamental tool in the oil and gas industry to ensure the efficiency and safety of equipment. This paper presents a comprehensive literature review on current methodologies for identifying safety critical equipment (SCE) and critical equipment (CE). The review begins by discussing the initial step of identifying SCE, followed by the identification of CE. Various methodologies are examined, ranging from simple, quick screening techniques to complex, quantitative analyses. Simple methodologies are often used for initial assessments, while complex methods are applied to equipment posing high risks that require detailed, quantitative calculations. The review highlights the strengths and limitations of each approach, providing a comparative analysis of their effectiveness in different scenarios. Finally, the paper suggests a structured methodology for developing maintenance tasks tailored specifically for SCE and CE, ensuring optimal maintenance strategies that enhance safety and operational efficiency. This review aims to guide practitioners in selecting appropriate methodologies for criticality analysis and maintenance planning in the oil and gas sector.

The Catatonia Quick Screen (CQS): A Rapid Screening Tool for Catatonia in Adult and Pediatric Populations.

Catatonia is a neuropsychiatric disorder marked by significant disturbances in motor, cognitive, and affective functioning and that is frequently under-diagnosed. To enhance clinical detection of catatonia, this study aimed to develop a rapid, sensitive Catatonia Quick Screen (CQS) using a reduced set of catatonic signs to facilitate screening in adult and pediatric patients. Data were derived from two retrospective cohorts totaling 446 patients (254 adults, 192 children) who screened positive for catatonia using the Bush Francis Catatonia Screening Instrument (BFCSI). Sensitivity analyses were performed for all combinations of BFCSI signs, with sensitivity defined as the proportion of patients identified by each subset relative to the full BFCSI. The CQS was developed by selecting signs from the BFCSI based on sensitivity, ease of assessment, and relevance to diverse catatonia presentations. Screening for the presence of any one of four signs-excitement, mutism, staring, or posturing-using the CQS yielded a theoretical sensitivity of 97% (95% CI: 95 to 98%) relative to the full BFCSI (which requires two signs out of 14). The CQS demonstrated 97% sensitivity across both pediatric and adult subsets. The Catatonia Quick Screen provides a rapid screening alternative to the BFCSI with high sensitivity, potentially improving early detection of catatonia in clinical settings. Future prospective studies are necessary to validate the CQS's sensitivity and to determine its specificity in clinical populations. The Catatonia Quick Scale (CQS) is a newly developed rapid screen for catatonia.Assessment for any one of four signs-excitement, mutism, staring, and posturing-has 97% sensitivity for detecting catatonia relative to the BFCSI.This screening is sensitive in both adult and pediatric populations. The study relies on retrospective data.Only patients who initially screened positive for catatonia were included, so the specificity of the CQS could not be assessed.Most of the sample has a primary psychiatric diagnosis so the generalizability to patients with primary medical disorders is unclear.

Using the PROMOTE Screener to Identify Psychosocial Risk Factors for Prenatal Substance Use

Introduction There is an urgent need to improve the identification of psychosocial vulnerabilities in clinical practice (eg, stress, unstable living conditions) and examine their contribution to prenatal substance use, especially for legal substances such as alcohol, tobacco, and recently, cannabis. Methods We conducted a retrospective chart review of 1842 patients who completed the PROMOTE screening instrument during their first prenatal visit to outpatient clinics of a New York State health system in 6/2019–11/2020. The PROMOTE includes 18 core items to assess psychosocial vulnerabilities including the NIDA Quick Screen assessing past year substance use. Outcomes were tobacco, cannabis, and alcohol use during pregnancy based on electronic medical record abstraction including clinical notes, self-report, or urine toxicology. Results A total of 188 (10.2%) patients used at least 1 substance prenatally, including 132 (7.2%) tobacco, 50 (2.7%) cannabis, and 45 (2.4%) alcohol. Two of the NIDA Quick Screen items (past year tobacco use and past year illegal drug use) were associated in the bivariate analysis with greater use risk of all 3 substances. Additional risk factors uniquely associated with specific prenatal substance use variables include low education predicting tobacco use (adjusted odds ratio [AOR] = 2.74, 95% confidence interval [CI] = 1.43–5.23), being unpartnered predicting cannabis use (AOR = 3.37, 95% CI = 1.21–9.39), and major life events predicting alcohol use (AOR = 3.25, 95% CI = 1.439–7.38). Conclusions Utilizing brief psychosocial self-screening instruments such as the PROMOTE can help identify and refer at-risk patients to appropriate care. Particular attention should be paid to life context including partner support, emotional health, stress, and past year substance use.

Machine learning assisted screening of nitrogen-doped graphene-based dual-atom hydrogen evolution electrocatalysts

Nitrogen doped graphene-based dual-atom catalysts (NG-DACs) usually exhibit high reactivity for hydrogen evolution reaction (HER). The experimental design of efficient DACs-based HER catalysts, however, is time-consuming and expensive. In this work, a density functional theory combined with machine learning (DFT-ML) strategy is adopted towards the rapid screening of NG-DACs HER catalyst. The adsorption energies of HER intermediates on NG-DACs obtained by DFT calculations and their elemental features were used for the ML database construction. The prediction performance of three ML algorithms was evaluated, and gradient boosted regression (GBR) is found exhibit the best prediction accuracy. Using the HER energetics on Pt (111) as reference, quick screening of the NG-DACs with high HER activity was achieved, resulting to twenty-four potential catalysts. Further selection and validation of the ML-screened catalysts were performed by the hydrogen adsorption Gibbs free energy analysis. Accordingly, four promising NG-DACs HER catalysts were predicted using the proposed DFT-ML based catalyst prediction framework. The DFT-ML strategy offers an promising approach for the screening of new HER electrocatalysts.

The quick screening of binding compounds and proteins for drug discovery and pharmacological research

In drug discovery and pharmacological research, early identification of target molecules for compounds with pharmacological effects is crucial. However, this process often requires significant effort and can be rate-limiting, thereby slowing down research progress. This paper introduces a simplified and rapid method for quick screening of binding compounds or proteins. Utilizing Affinity Selection Mass Spectrometry (ASMS), this technique efficiently detects compound-target binding through size-exclusion chromatography and mass spectrometry. ASMS offers high sensitivity and specificity, making it ideal for accurate identification of binding interactions. We have further enhanced ASMS to handle membrane proteins without solubilization, creating Binder Selection Technology (BST). BST allows screening against both soluble and challenging membrane proteins such as GPCRs and SLC transporters. By using cell membrane fractions or organelle fractions with high target molecule expression, BST efficiently identifies potential binding compounds. This innovative method constructs a comprehensive database of binding compounds for various targets, facilitating rapid hypothesis testing and pharmacological evaluation. Additionally, BST screens 17,000 proteins, including membrane proteins, using wheat germ cell-free and animal cell expression systems. This approach allows exploration of binding interactions without labeling compounds or immobilizing proteins, preserving their native state. BST is powerful for identifying targets of compounds with known pharmacological effects but unknown targets in animal or cell-based assays. By utilizing BST, researchers can overcome bottlenecks in early drug discovery, significantly enhancing research speed and success rates. This method represents a major advancement, providing an efficient and effective way to identify and validate target molecules in drug discovery.

Research work of Prof. De-Eknamkul, including the mechanistic synergy of hair growth promotion by the Avicennia marina extract and its active constituent (avicequinone C) in dermal papilla cells isolated from androgenic alopecia patients

Androgenetic alopecia (AGA) is a genetically-predetermined disorder that affects many people around the world, leading to progressive hair loss. This baldness can negatively impact people’s perceptions of themselves and scientists are searching for a cure. There are only two US Food and Drug Administration-approved drugs used to treat AGA and they both have side effects. If a natural anti-AGA product could be developed with the ability to be effective minus negative side effects, this could be a game-changer for people with AGA. Professor Wanchai De-Eknamkul, Department of Pharmacognosy and Pharmaceutical Botany, Chulalongkorn University, Thailand, an expert in plant natural product biotechnology, is working to develop a natural anti-AGA product. He is a professor at the University’s Natural Product Biotechnology Research Unit, where research is underway to: elucidate the biosynthesis of bioactive natural products by searching for enzymes and genes involved in their biosynthetic pathways; discover bioactive natural compounds from medicinal plants by either enzyme-based or cell-based screening followed by studying their mechanism of actions; and develop high-performance thin-layer chromatography (HPTLC) as a tool for quick screening and identification of bioactive compounds. De-Eknamkul have been working to shed light on the genes and enzymes involved in the biosynthesis of the diterpenoid ‘plaunotol’ in Croton stellatopilosus. In another line of investigation, they are searching for natural compounds with anti-AGA and anti-grey hair activities and a further area of focus is the development of a technique for direct detection of compounds with anti-diabetes, anti-gonorrhoea and anti-AGA activities.

Development and validation of the four-item Concise Aging adults Smell Test to screen for olfactory dysfunction in older adults.

Olfactory dysfunction (OD) in the elderly is common and associated with numerous comorbidities, yet often underrecognized. This study sought to develop an instrument for widespread screening of this condition. Demographic factors, comorbidities, and olfactory-related questions were examined in a focus group (N=20) and development cohort (N=190). The 4-item Concise Aging adults Smell Test (4-CAST) screening instrument was then developed to predict OD on Sniffin Sticks testing. An independent validation cohort (N=147) was then examined to confirm ability of the 4-CAST to predict OD on Smell Identification Test 40 (SIT40). The most important variables for predicting olfactory loss in older subjects were age, type II diabetes status, Visual Analog Scale (VAS) of overall rating of smell, and VAS of impact of smell upon safety. In the development cohort, the 4-CAST model yielded predictive probability (area under the curve) of 0.805 (p<0.001) for predicting the bottom 25th percentile on Sniffin Sticks. Results were similar in the validation cohort, as the 4-CAST accurately classified 83.8% subjects (area under the curve [AUC] 0.789, p<0.001) in the bottom 25th percentile of SIT40 testing. The 4-CAST was able to accurately classify 89.4% of subjects with anosmia. The 4-CAST is a quick screening instrument for predicting OD in older adults and has similar performance characteristics in independent populations using the two most common validated olfactory tests. Given the high prevalence of unrecognized olfactory loss in this population, this can be a useful tool for practitioners to determine which subjects may benefit from more extensive olfactory testing.

Rapid screening and identification strategy of lactic acid bacteria and yeasts based on Ramanomes technology and its application in fermented food

There is an urgent need for quick, sensitive, thorough, and low-cost preliminary screening and rapid identification method for probiotic resource development. Here, we constructed a culture-free, accurate and sensitive “separation-enrichment-detection” rapid evaluation and identification method of probiotics in fermented food based on Ramanomes technology, and established a Ramanome reference of lactic acid bacteria and yeasts by AgNPs nanostructure array (AgNPs NSA) chips with uniform “hot spots” and high signal enhancement ability as SERS substrates. Systematic cluster analysis could clearly distinguish among different genera of lactic acid bacteria and yeast, and could indicate the affinity and difference between lactic acid bacteria of the same genus and yeast of the same genus. The recognition accuracy of convolutive neural networks was 100 %, and the recognition sensitivity was less than 10 CFU/mL. We constructed a probiotic isolation and enrichment method by velocity gradient centrifugation and density gradient differential centrifugation, and the average recoveries of lactobacilli and yeasts were greater than 98 % in the practical application, and the accuracy of the verified classification was 100 %. In conclusion, this study has established a preliminary screening strategy of “screening before cultivating” for lactic acid bacteria and yeasts, which breaks through the principle limitation of the current traditional paradigm of “cultivating before screening”. It can greatly improve the preliminary screening rate of probiotics in fermented foods, and provide a good technical support for the mining of probiotic resources from environmental or complex matrix samples.

A Balanced Mission Planning for Multiple Unmanned Underwater Vehicles in Complex Marine Environments

The collaboration of a multiple unmanned underwater vehicles (multi-UUVs) system has attracted widespread attention in recent years, as it can overcome the limitations of a single UUV and enhance mission completion efficiency. Oriented towards patrol and exploration missions with multiple waypoints, this paper proposes a balanced mission planning strategy, aiming to improve mission quality while reducing mission time for multi-UUVs. Firstly, due to the uneven performance of the two optimization objectives, a quick initialization screening method is employed specifically for mission quality to reduce the mission space. Secondly, to ensure mission load distribution and collaboration among multi-UUVs, and ease the difficulty in solving the issues of mission allocation and route planning, a balanced bi-level mission planning method based on regional segmentation is proposed. Finally, applicable weight evaluation criteria are utilized to evaluate the feasible solution set and determine the optimal solution. The efficacy of the balanced mission planning strategy is substantiated through comprehensive numerical simulations in a complex 2D marine environment.

A phase separation prediction model for CO2 capture by biphasic amine absorbents

The biphasic amine absorbent upon absorbing CO2, separates into CO2 poor and CO2 rich phases, making it a third-generation chemical absorbent with energy-saving potential. However, the current method of constructing biphasic amine absorbents involves screening phase separation agents through a large number of experiments, which involves significant amount of trial and error. In this study, a phase separation prediction model was built for CO2 capture by biphasic amine absorbents for quick screening of phase separation agents. The phase separation of CO2 absorption by organic amine 2-amino-2-methyl-1-propanol (AMP) using 34 phase separation agents was statistically analyzed based on 14 molecular descriptors of the solvents. The factors influencing the phase separation were thoroughly explored. It is proposed that the relative dielectric constant or molecular polarity index can replace the traditional polarity evaluation index, dipole moment, to better describe the phase separation situation. Additionally, it is found that the ability of phase separation agents to form hydrogen bonds, particularly as a hydrogen bond donor is the determining factor for phase separation to occur. Ultimately, the preferred molecular descriptors are used as predictors, leading to the development of a prediction model for phase separation that achieves high-accuracy predictions solely through theoretical calculation parameters. The model considerably reducing the construction costs of biphasic amine absorbents.

Population Characteristics of the Spectrum and Frequencies of CFTR Gene Mutations in Patients with Cystic Fibrosis from the Republic of Bashkortostan (Russia).

Cystic fibrosis (CF) is one of the most common autosomal-recessive disorders worldwide. The incidence of CF depends on the prevalence of cystic fibrosis transmembrane conductance regulator gene (CFTR) mutations in the population, which is determined by genetic diversity and ethnicity. The search for the causes of mutations in the transmembrane conductance regulator gene (CFTR) was carried out using targeted next-generation sequencing (NGS) on the Illumina platform in patients with cystic fibrosis from the Republic of Bashkortostan (Russia), taking into account the ethnic structure of the sample. A total of 35 distinct causal variants were found in 139 cases from 129 families. Five (F508del, E92K, 3849+10kbC>T, CFTRdele2.3, L138ins) explain 78.7% of identified CF causal alleles. Variants N13103K and 394delTT were found in four families each. Variants 2143delT, S1196X, W1282X, Y84X, G194R, and 1525-1G>A, as well as the two previously described complex alleles-c. [S466X; R1070Q] and str.[G509D;E217G]-were found in two or three families each. Twenty additional variants occurred only once. Variant c.3883_3888dup has not been described previously. Thus, regional and ethnic features were identified in the spectrum of frequencies of pathogenic variants of the CFTR gene in the three major sub-groups of patients-Russians, Tatars, and Bashkirs. Taking into account these results, highlighting the genetic specificity of the region, a more efficient search for CFTR mutations in patients can be performed. In particular it is possible to choose certain test kits for quick and effective genetic screening before use of NGS sequencing.

Nutritional Assessment Based on Mid-upper Arm Circumference Among Tribal Preschool Children of Three Distinct Ecological Zones of India

Undernutrition among infants and children retards the physical and cognitive growth and increases susceptibility to infections and diseases. Educational attainment and productivity of children get adversely affected by undernutrition, NFHS (2019–2020) and IIPS (2021) found the prevalence of undernutrition indicators to be high among the under-five children of the nation and tribal children to be in a more vulnerable condition. Mean mid-upper arm circumference (MUAC) is a quick, simple and precise screening tool to use in large-scale community-based nutritional programme. The present study aimed to assess the prevalence of undernutrition based on MUAC among three tribes of three distinct ecological zones of India, namely Bhil Tribe (Desert ecology), Dhodia tribe (Coastal ecology) and Kinnaura tribe (Himalayan ecology). The present cross-sectional study consists of 985 tribal children (509 boys and 476 girls) belonging to the age group 0–1 through 5+ years (below six years). Z-score was computed using WHO (2006) recommended age and sex-specific cutoff points for MUAC and the prevalence of undernutrition was assessed for children of below five years of age. MUAC has been found to have significant age difference among all communities ( p &lt; .001). Prevalence of undernutrition was found to be the highest among the Bhil tribe, the one residing in the desert ecology (41.03%) followed by Dhodia (18.06%) and Kinnaura tribe (12.16%). Among Bhil and Kinnaura children, prevalence of undernutrition was higher among the girls, while the Kinnaura boys manifested higher prevalence of undernutrition in comparison to their counterpart. Significant ( p &lt; .01) sex difference in undernutrition prevalence was found only in case of Bhil tribe (boys: 33.33%, girls: 48.02%). The prevalence of undernutrition significantly differs among the tribes of three distinct ecological zones ( p &lt; .001). The nutritional status of the tribal children, particularly that of the Bhil children, was unsatisfactory. To improve the nutritional wellbeing of the tribal population of the nation, studies at regular intervals focusing on the causes responsible for nutritional status are needed.

Simple rules and heuristic decision making using the quick screen tool: A JD Bioscience case study

ABSTRACT This case study examines JD Bioscience (JDB), a South Korean pharmaceutical start-up, using the quick screen framework and its scorecard tool. The underlying rationale (the “why”) is to combat the decision-making complexities involved with new assets for licensing and acquisition within the life science industry by utilizing simple rules (the “how”) via the quick screen tool (the “what”). This article aims to explore simple rules and heuristic decision making in life science opportunities. JDB’s final score of 2.87 reflects unique attributes and developmental risks around the firm, its lead asset, and its management team. The analysis supports the application of a simple rules decision-making framework for entrepreneurs, investors, and executives. This framework identifies an opportunity’s maturation stage and roadblocks the venture needs to address. The findings shed light on JDB’s trajectory and underscore the significance of employing strategic decision-making tools in navigating the complexities of the life sciences landscape.

Morphological and molecular diversity analysis vis-a-vis phylogenetic correlation among pomegranate (Punica granatum L.) varieties

Abstract Identification of different pomegranate (Punica granatum L.) genotypes using morphological descriptors and further substantiated through molecular means is essential for a breeding program. Characterization using molecular markers has an advantage over its morphological counterpart due to its low expense, quick and reliable results, and the requirement of less sophisticated equipment. In this study, eight different pomegranate cultivars were characterized based on their morphological and molecular characters in sub-tropical regions between 2018 and 2019. Morphological experiments were conducted in a randomized block design replicated thrice for each cultivar, whereas RAPD markers were employed for molecular studies. Principal component analysis revealed significant morphological variation (79.61%) among the cultivars. A high level of polymorphism, similar to PCA, was observed with RAPD analysis (80.49%). Some discrepancies between morphological and molecular results were noted in UPGMA cluster analysis. However, eight primers (total = 41) distinctively separated the Bhagwa variety from others, with the maximum similarity coefficient value (ca. 0.54) and superior fruit quality distinguished in PCA analysis with the maximum mean Euclidean distance (ca. 16). Hence, RAPD markers could be used along with morphological markers for a quick screening of germplasms in further crop improvement programs.

The Use of Artificial Intelligence for Estimating Anterior Chamber Depth from Slit-Lamp Images Developed Using Anterior-Segment Optical Coherence Tomography.

Primary angle closure glaucoma (PACG) is a major cause of visual impairment, particularly in Asia. Although effective screening tools are necessary, the current gold standard is complex and time-consuming, requiring extensive expertise. Artificial intelligence has introduced new opportunities for innovation in ophthalmic imaging. Anterior chamber depth (ACD) is a key risk factor for angle closure and has been suggested as a quick screening parameter for PACG. This study aims to develop an AI algorithm to quantitatively predict ACD from anterior segment photographs captured using a portable smartphone slit-lamp microscope. We retrospectively collected 204,639 frames from 1586 eyes, with ACD values obtained by anterior-segment OCT. We developed two models, (Model 1) diagnosable frame extraction and (Model 2) ACD estimation, using SWSL ResNet as the machine learning model. Model 1 achieved an accuracy of 0.994. Model 2 achieved an MAE of 0.093 ± 0.082 mm, an MSE of 0.123 ± 0.170 mm, and a correlation of R = 0.953. Furthermore, our model's estimation of the risk for angle closure showed a sensitivity of 0.943, specificity of 0.902, and an area under the curve (AUC) of 0.923 (95%CI: 0.878-0.968). We successfully developed a high-performance ACD estimation model, laying the groundwork for predicting other quantitative measurements relevant to PACG screening.

Current Perspectives on Olfactory Loss in Atypical Parkinsonisms-A Review Article.

Atypical parkinsonisms (APs) present various symptoms including motor impairment, cognitive decline, and autonomic dysfunction. Olfactory loss (OL), being a significant non-motor symptom, has emerged as an under-evaluated, yet potentially valuable, feature that might aid in the differential diagnosis of APs. The most pronounced OL is usually associated with Dementia with Lewy Bodies (DLB). While the view about the normosmic course of Multiple System Atrophy (MSA) remains unchanged, research indicates that mild OL may occur in a subset of patients with Progressive Supranuclear Palsy (PSP) and Corticobasal Degeneration (CBD). This might be linked to the deposition of abnormal protein aggregates in the central nervous system. The aim of this review is to discuss the role of OL and its degree and pattern in the pathogenesis and course of APs. Olfactory testing could serve as a non-invasive, quick screening tool to differentiate between APs and project disease progression. There is a need for further evaluation of this topic. This may lead to the development of standardized olfactory testing protocols that could be implemented in clinical practice, making differential diagnosis of APs more convenient. Understanding differences in the sense of smell could create an avenue for more targeted therapeutic strategies.