Integration of MALDI-TOF MS and machine learning to classify enterococci: A comparative analysis of supervised learning algorithms for species prediction.

This research focused on distinguishing distinct matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) spectral signatures of three Enterococcus species. We evaluated and compared the predictive performance of four supervised machine learning algorithms, K-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF), to accurately classify Enterococcus species. This study involved a comprehensive dataset of 410 strains, generating 1640 individual spectra through on-plate and off-plate protein extraction methods. Although the commercial database correctly identified 76.9% of the strains, machine learning classifiers demonstrated superior performance (accuracy 0.991). In the RF model, top informative peaks played a significant role in the classification. Whole-genome sequencing showed that the most informative peaks are biomarkers connected to proteins, which are essential for understanding bacterial classification and evolution. The integration of MALDI-TOF MS and machine learning provides a rapid and accurate method for identifying Enterococcus species, improving healthcare and food safety.

One-class modelling applied to ATR-FTIR spectroscopy for determining document forgeries related to paper aging.

One of the great challenges of document analysis is determining document forgeries. The present work proposes a non-destructive approach to discriminate natural and artificially aged papers using infrared spectroscopy and soft independent modeling by class analogy (SIMCA) algorithms. This is of particular interest in cases of document falsifications made by artificial aging, for this study, SIMCA, and Data-Driven SIMCA (DD-SIMCA) classification models were built using naturally aged paper samples, taken from three time periods: 1st period from 1998 to 2003; 2nd period from 2004 to 2009; and 3rd period from 2010 to 2015. Artificially aged samples (exposed to high temperature or UV radiation) were used as test sets. Promising results in detecting document falsifications related to aging were obtained. Samples artificially aged at high temperature were correctly discriminated from the authentic samples (naturally aged) with 100% accuracy. In contrast, the samples under the photodegradation process showed a lower classification performance, with results above 90%.

Rising frequency of ozone-favorable synoptic weather patterns contributes to 2015-2022 ozone increase in Guangzhou.

Objective weather classification methods have been extensively applied to identify dominant ozone-favorable synoptic weather patterns (SWPs), however, the consistency of different classification methods is rarely examined. In this study, we apply two widely-used objective methods, the self-organizing map (SOM) and K-means clustering analysis, to derive ozone-favorable SWPs at four Chinese megacities in 2015-2022. We find that the two algorithms are largely consistent in recognizing dominant ozone-favorable SWPs for four Chinese megacities. In the case of classifying six SWPs, the derived circulation fields are highly similar with a spatial correlation of 0.99 between the two methods, and the difference in the mean frequency of each SWP is less than 7%. The six dominant ozone-favorable SWPs in Guangzhou are all characterized by anomaly higher radiation and temperature, lower cloud cover, relative humidity, and wind speed, and stronger subsidence compared to climatology mean. We find that during 2015-2022, the occurrence of ozone-favorable SWPs days increases significantly at a rate of 3.2 day/year, faster than the increases in the ozone exceedance days (3.0 day/year). The interannual variability between the occurrence of ozone-favorable SWPs and ozone exceedance days are generally consistent with a temporal correlation coefficient of 0.6. In particular, the significant increase in ozone-favorable SWPs in 2022, especially the Subtropical High type which typically occurs in September, is consistent with a long-lasting ozone pollution episode in Guangzhou during September 2022. Our results thus reveal that enhanced frequency of ozone-favorable SWPs plays an important role in the observed 2015-2022 ozone increase in Guangzhou.

Radiomics analysis for prediction and classification of submucosal tumors based on gastrointestinal endoscopic ultrasonography.

Objectives: To identify and classify submucosal tumors by building and validating a radiomics model with gastrointestinal endoscopic ultrasonography (EUS) images. Methods: A total of 144 patients diagnosed with submucosal tumors through gastrointestinal EUS were collected between January 2019 and October 2020. There are 1952 radiomic features extracted from each patient's EUS images. The statistical test and the customized least absolute shrinkage and selection operator regression were used for feature selection. Subsequently, an extremely randomized trees algorithm was utilized to construct a robust radiomics classification model specifically tailored for gastrointestinal EUS images. The performance of the model was measured by evaluating the area under the receiver operating characteristic curve. Results: The radiomics model comprised 30 selected features that showed good discrimination performance in the validation cohorts. During validation, the area under the receiver operating characteristic curve was calculated as 0.9203 and the mean value after 10-fold cross-validation was 0.9260, indicating excellent stability and calibration. These results confirm the clinical utility of the model. Conclusions: Utilizing the dataset provided curated from gastrointestinal EUS examinations at our collaborating hospital, we have developed a well-performing radiomics model. It can be used for personalized and non-invasive prediction of the type of submucosal tumors, providing physicians with aid for early treatment and management of tumor progression.

ICF-based hearing and functioning assessment: validation and research outcomes of utilizing the HEAR-COMMAND tool for patients with mild to moderately severe hearing loss and individuals with normal hearing.

Objective: Current clinical assessments for Hearing Loss (HL) are often limited to controlled laboratory settings in which a narrow spectrum of hearing difficulties can be assessed. A majority of the daily life challenges caused by HL cannot be measured in clinical methodologies. To screen the individuals' needs and limitations, a questionnaire named the HEAR-COMMAND tool was developed and qualitatively validated through an international collaboration, aligning with the World Health Organization's International Classification of Functioning, Disability, and Health Framework (ICF) Core Sets for Hearing Loss. The tool empowers healthcare professionals (HCPs) to integrate the ICF framework into patient assessments and patient-reported outcomes (PRO) in clinical and non-clinical settings. The aim is to provide a general foundation and starting point for future applications in various areas including ENT and hearing acoustics. The outcome can be employed to define and support rehabilitation in an evidence-based manner. This article presents the validation and research outcomes of using the tool for individuals with mild to moderately severe HL in contrast to normal-hearing individuals. Design: Using a cross-sectional multicenter study, the tool was distributed among 215 participants in Germany, the USA, and Egypt, filled in German, English, or Arabic. Three outcome scores and the corresponding disability degree were defined: hearing-related, non-hearing-related, and speech-perception scores. The content and construct validation were conducted, and the tool's internal consistency was assessed. Results: The extracted constructs included "Auditory processing functionality", "Sound quality compatibility", "Listening and communication functionality", "Interpersonal interaction functionality and infrastructure accessibility", "Social determinants and infrastructure compatibility", "Other sensory integration functionality", and "Cognitive functionality". Regarding content validity, it was demonstrated that normal-hearing participants differed significantly from individuals with HL in the hearing-related and speech-perception scores. The reliability assessment showed a high internal consistency (Cronbach's alpha = 0.9). Conclusion: The outcome demonstrated the HEAR-COMMAND tool's high content and construct validity. The tool can effectively represent the patient's perspective of HL and hearing-related functioning and enhance the effectiveness of the treatment plans and rehabilitation. The broad range of targeted concepts provides a unique overview of daily life hearing difficulties and their impact on the patient's functioning and quality of life.

A 25-Year Retrospective of the Use of AI for Diagnosing Acute Stroke: Systematic Review.

BACKGROUND: Stroke is a leading cause of death and disability worldwide. Rapid and accurate diagnosis is crucial for minimizing brain damage and optimizing treatment plans. OBJECTIVE: This review aims to summarize the methods of artificial intelligence (AI)-assisted stroke diagnosis over the past 25 years, providing an overview of performance metrics and algorithm development trends. It also delves into existing issues and future prospects, intending to offer a comprehensive reference for clinical practice. METHODS: A total of 50 representative articles published between 1999 and 2024 on using AI technology for stroke prevention and diagnosis were systematically selected and analyzed in detail. RESULTS: AI-assisted stroke diagnosis has made significant advances in stroke lesion segmentation and classification, stroke risk prediction, and stroke prognosis. Before 2012, research mainly focused on segmentation using traditional thresholding and heuristic techniques. From 2012 to 2016, the focus shifted to machine learning (ML)-based approaches. After 2016, the emphasis moved to deep learning (DL), which brought significant improvements in accuracy. In stroke lesion segmentation and classification as well as stroke risk prediction, DL has shown superiority over ML. In stroke prognosis, both DL and ML have shown good performance. CONCLUSIONS: Over the past 25 years, AI technology has shown promising performance in stroke diagnosis.

Deep Learning-Based Image Classification and Segmentation on Digital Histopathology for Oral Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis.

BACKGROUND: Artificial intelligence (AI)-based tools have shown promise in histopathology image analysis in improving the accuracy of oral squamous cell carcinoma (OSCC) detection with intent to reduce human error. OBJECTIVES: This systematic review and meta-analysis evaluated deep learning (DL) models for OSCC detection on histopathology images by assessing common diagnostic performance evaluation metrics for AI-based medical image analysis studies. METHODS: Diagnostic accuracy studies that used DL models for the analysis of histopathological images of OSCC compared to the reference standard were analyzed. Six databases (PubMed, Google Scholar, Scopus, Embase, ArXiv, and IEEE) were screened for publications without any time limitation. The QUADAS-2 tool was utilized to assess quality. The meta-analyses included only studies that reported true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) in their test sets. RESULTS: Of 1267 screened studies, 17 studies met the final inclusion criteria. DL methods such as image classification (n = 11) and segmentation (n = 3) were used, and some studies used combined methods (n = 3). On QUADAS-2 assessment, only three studies had a low risk of bias across all applicability domains. For segmentation studies, 0.97 was reported for accuracy, 0.97 for sensitivity, 0.98 for specificity, and 0.92 for Dice. For classification studies, accuracy was reported as 0.99, sensitivity 0.99, specificity 1.0, Dice 0.95, F1 score 0.98, and AUC 0.99. Meta-analysis showed pooled estimates of 0.98 sensitivity and 0.93 specificity. CONCLUSION: Application of AI-based classification and segmentation methods on image analysis represents a fundamental shift in digital pathology. DL approaches demonstrated significantly high accuracy for OSCC detection on histopathology, comparable to that of human experts in some studies. Although AI-based models cannot replace a well-trained pathologist, they can assist through improving the objectivity and repeatability of the diagnosis while reducing variability and human error as a consequence of pathologist burnout.

What is the Difference between Conventional Drinking Water, Potable Reuse Water, and Nonpotable Reuse Water? A Microbiome Perspective.

As water reuse applications expand, there is a need for more comprehensive means to assess water quality. Microbiome analysis could provide the ability to supplement fecal indicators and pathogen profiling toward defining a "healthy" drinking water microbiota while also providing insight into the impact of treatment and distribution. Here, we utilized 16S rRNA gene amplicon sequencing to identify signature features in the composition of microbiota across a wide spectrum of water types (potable conventional, potable reuse, and nonpotable reuse). A clear distinction was found in the composition of microbiota as a function of intended water use (e.g., potable vs nonpotable) across a very broad range of U.S. water systems at both the point of compliance (Betadisper p > 0.01; ANOSIM p < 0.01, r-stat = 0.71) and point of use (Betadisper p > 0.01; ANOSIM p < 0.01, r-stat = 0.41). Core and discriminatory analysis further served in identifying distinct differences between potable and nonpotable water microbiomes. Taxa were identified at both the phylum (Desulfobacterota, Patescibacteria, and Myxococcota) and genus (Aeromonas and NS11.12_marine_group) levels that effectively discriminated between potable and nonpotable waters, with the most discriminatory taxa being core/abundant in nonpotable waters (with few exceptions, such as Ralstonia being abundant in potable conventional waters). The approach and findings open the door to the possibility of microbial community signature profiling as a water quality monitoring approach for assessing efficacy of treatments and suitability of water for intended use/reuse application.

Articulated ankle-foot orthoses associated with home-based task-specific training improve functional mobility in patients with stroke: a randomized clinical trial.

INTRODUCTION: We compared fixed and articulated ankle-foot orthoses (AFOs) in home-based mobility tasks to assess short-term mobility, dynamic balance, quality of life, anxiety/depression, disability level, stroke severity, autonomy, human functioning, and patient satisfaction. METHODS: This was a two-arm, parallel-group, randomized controlled trial with concealed allocation, assessor blinding, and a complete case analysis involving patients with chronic stroke. The participants were randomized into two groups: fixed (n = 24) and articulated (n = 23) AFOs. The AFOs were custom-fabricated, and both groups performed four-week home-based mobility tasks five days weekly. Primary outcome measures included changes in balance and mobility assessed using the Tinetti Performance-Oriented Mobility Assessment (POMA), Timed Up and Go (TUG) test, and Functional Ambulation Category (FAC). Secondary outcomes included quality of life, anxiety/depression, disability, stroke severity, autonomy, human functioning, and patient satisfaction. RESULTS: In a between-group comparison, after adjusting for age, sex, stroke severity, and thrombolysis, the articulated AFO group showed better performance in the TUG test (p = 0.020; d = 0.93), POMA-Gait (p = 0.001; d = 0.53), POMA-Total (p = 0.048; d = 0.98), and FAC (p = 0.003; d = 1.03) than the fixed AFO group. Moreover, significant difference was noted in human functioning (moving around using equipment)between the groups (p = 0.047; d = 92). CONCLUSION: A program involving home-based mobility tasks and articulated AFOs improved functional mobility after stroke.

Development and Preliminary Validation of a Central Venous Access Device-Associated Skin Impairment Classification Tool Using Modified Delphi and Clinimetric Methods.

BACKGROUND: An evidence and consensus-based instrument is needed to classify central venous access device-associated skin impairments. AIM: The aim of this study was to design and evaluate the central venous access device-associated skin impairment classification tool. DESIGN: A two-phase modified Delphi study. METHODS: This two-phase study consisted of a literature review, followed by the development and validation of a classification instrument, by experts in the fields of central venous access devices and wound management (Phase 1). The instrument was tested (Phase 2) using 38 clinical photographs of a range of relevant skin impairments by the same expert panel. The expert panel consisted of registered nurses who were clinical researchers (n = 4) and clinical experts (n = 3) with an average of 24 years of nursing and research experience and 11 years of experience in wound management. Measures to assess preliminary content validity and inter-rater reliability were used. RESULTS: The instrument consists of five overarching aetiological classifications, including contact dermatitis, mechanical injury, infection, pressure injury and complex clinical presentation, with 14 associated subcategory diagnoses (e.g., allergic dermatitis, skin tear and local infection), with definitions and signs and symptoms. High agreement was achieved for preliminary scale content validity and item content validity (I-CVI = 1). Inter-rater reliability of aetiologies was high. The overall inter-rater reliability of individual definitions and signs and symptoms had excellent agreement. CONCLUSION: The development and preliminary validation of this classification tool provide a common language to guide the classification and assessment of central venous access device-associated skin impairment. IMPACT: The comprehensive and validated classification tool will promote accurate identification of central venous access device-associated skin impairment by establishing a common language for healthcare providers. The availability of this tool can reduce clinical uncertainty, instances of misdiagnosis and the potential for mismanagement. Consequently, it will play a pivotal role in guiding clinical decision-making, ultimately enhancing the quality of treatment and improving patient outcomes. REPORTING METHOD: The Guidance on Conducting and Reporting Delphi Studies (CREDES) was adhered to. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.

Classification of hallux valgus deformity-is there a standard?

INTRODUCTION: Hallux valgus deformity severity is one determent for the surgical procedure for hallux valgus (HV) correction. HV deformities are usually classified into mild/moderate/severe. The aim was to investigate the cut-off criteria used to classify HV deformity. MATERIALS AND METHODS: The study was based on a previous living systematic review. Four common databases were searched for the last decade. All review-steps were conducted by two reviewers. Data assessed were the individual cut-off values used to classify HV deformity into mild/moderate/severe, and the referenced classification systems. RESULTS: 46 studies were included. 21/18 studies grade deformity based on the intermetatarsal angle (IMA)/ hallux valgus angle (HVA) with great heterogeneity throughout the different cut-off values. The most referenced classification systems were the Coughlin and Mann's and the Robinson classification. CONCLUSIONS: The currently used classification systems are heterogenic, and no standard could be defined. The community should define a uniform classification system. LEVEL OF EVIDENCE: Level I, systematic review of randomized controlled trials and prospective comparative studies.

A novel radiological index uses the inner canal diameter and the Citak classification index to predict risk factor for aseptic loosening following hinged total knee arthroplasty.

INTRODUCTION: It remains unclear if distal femoral morphology should be a key consideration when selecting the implant or fixation strategy. A novel radiological index has been proposed to classify patients' distal femoral morphology. This study aims to evaluate the validity of this classification system in a cohort of patients undergoing hinged Total Knee Arthroplasty (TKA), and to determine if distal femoral morphology is a risk factor for aseptic loosening or all cause revision following hinged TKA. MATERIALS AND METHODS: This study was a retrospective analysis of our institutional database. Fifty-nine patients having undergone hinged TKA with adequate radiographs for examination were eligible for inclusion. Radiographic measurements were performed using the Citak radiological index criteria. The proportion of aseptic loosening and all-cause revisions were compared between the different classification groups. RESULTS: The analysis included 41 females (69.5%) and 18 males (30.5%). The mean age of the participants was 71.2 years (SD = 12.6). For inner canal diameter patients were classified as: Type A (31/59, 53%), Type B (19/59, 32%), and Type C (9/59, 15%). For the Index Classification Group, patients were classified as: Group A (26/59, 44%), Group B (20/59, 34%), and Group C (13/59, 22%). There was no significant difference in overall revision rate between the three groups (χ2 = 3.25, P = .197 from a Chi-square test). There was a significantly higher rate of aseptic loosening in Group C compared to Groups A and B, with no significant difference between Groups A and B in terms of aseptic loosening rates (χ2 = 8.72, P = .013 from a Chi-square test). CONCLUSIONS: Distal femoral morphology plays an important role in the risk of aseptic loosening following hinged knee replacement, and should be considered when deciding implant type and fixation in these patients.

Enhancing Sports Injury Risk Assessment in Soccer Through Machine Learning and Training Load Analysis.

Sports injuries pose significant challenges in athlete welfare and team dynamics, particularly in high-intensity sports like soccer. This study used machine learning algorithms to assess non-contact injury risk in professional male soccer players from physiological and mechanical load variables. Twenty-five professional male soccer players with a first-time, non-contact muscle injury were included in this study. Recordings of external load (speed, distance, and acceleration/deceleration data) and internal load (heart rate) were obtained during all training sessions and official matches over a 4-year period. Machine learning model training and evaluation features were calculated for each of nine different metrics for a 28-day period prior to the injury and an equal-length baseline epoch. The acute surge in the values of each workload metric was quantified by the deviation of maximum values from the average, while the variations of cumulative workload over the last four weeks preceding injury were also calculated. Seven features were selected by the model as prominent estimators of injury incidence. Three of the features concerned acute load deviations (number of sprints, training load score-incorporating heart rate and muscle load- and time of heart rate at the 90-100% of maximum). The four cumulative load features were (total distance, high speed and sprint running distance and training load score). The accuracy of the muscle injury risk assessment model was 0.78, with a sensitivity of 0.73 and specificity of 0.85. Our model achieved high performance in injury risk detection using a limited number of training load variables. The inclusion, for the first time, of heart rate related variables in an injury risk assessment model highlights the importance of physiological overload as a contributor to muscle injuries in soccer. By identifying the important parameters, coaches may prevent muscle injuries by controlling surges of training load during training and competition.

Investigating the WHO's AWaRe classification for assessing antimicrobial stewardship programs: A single-center study.

BACKGROUND: AWaRe (Access, Watch, Reserve) classification proposed by the World Health Organization (WHO) holds potential for assessing antimicrobial stewardship programs (ASPs). However, increase in antibiotics for non-infectious treatment might undermine the effectiveness of using the AWaRe classification for assessing ASPs. The study aimed to evaluate the antimicrobial usage by AWaRe classification and specify issues for assessing ASPs. METHODS: The retrospective study was conducted in a single center within an 845-bed hospital. Antimicrobial usage data for outpatients were obtained from medical records used for billing purposes. Antimicrobials for non-infectious treatment were defined by smaller dose of macrolides, tetracyclines with pemphigoid, rifaximin, and prophylactic sulfamethoxazole-trimethoprim (ST) agent. RESULTS: The usage of antimicrobials for non-infectious treatment increased from 25.3 % to 50.1 % for the ratio of the amount to defined daily doses (DDDs) and from 46.3 % to 65.9 % for prescription days between January 2015 and March 2024. The usage of prophylactic sulfamethoxazole-trimethoprim (ST) agents increased by 2.4 times, and the usage of rifaximin increased by more than 100 times. Macrolides for non-infectious treatment was stable or fluctuated while that for infection treatment decreased to that amount for non-infectious treatment. The ratios for Access increased from 31.9 % to 58 % and 42 % to 78 % by excluding the antimicrobials for non-infectious treatment. CONCLUSIONS: The findings suggested that the AWaRe classification might not be appropriate for assessing ASPs and comparing them among hospitals.

Deep dual incomplete multi-view multi-label classification via label semantic-guided contrastive learning.

Multi-view multi-label learning (MVML) aims to train a model that can explore the multi-view information of the input sample to obtain its accurate predictions of multiple labels. Unfortunately, a majority of existing MVML methods are based on the assumption of data completeness, making them useless in practical applications with partially missing views or some uncertain labels. Recently, many approaches have been proposed for incomplete data, but few of them can handle the case of both missing views and labels. Moreover, these few existing works commonly ignore potentially valuable information about unknown labels or do not sufficiently explore latent label information. Therefore, in this paper, we propose a label semantic-guided contrastive learning method named LSGC for the dual incomplete multi-view multi-label classification problem. Concretely, LSGC employs deep neural networks to extract high-level features of samples. Inspired by the observation of exploiting label correlations to improve the feature discriminability, we introduce a graph convolutional network to effectively capture label semantics. Furthermore, we introduce a new sample-label contrastive loss to explore the label semantic information and enhance the feature representation learning. For missing labels, we adopt a pseudo-label filling strategy and develop a weighting mechanism to explore the confidently recovered label information. We validate the framework on five standard datasets and the experimental results show that our method achieves superior performance in comparison with the state-of-the-art methods.

Potentially Missed Diagnoses in Prenatal Versus Postnatal Exome Sequencing in the Lack of Informative Phenotype: Lessons Learned From a Postnatal Cohort.

OBJECTIVE: To investigate how many novel pathogenic (P) and likely pathogenic (LP) nonprotein-truncating or noncanonical splicing variants would be classified as variants of unknown significance (VUS) if they were detected in fetuses without abnormalities. METHODS: The study included 156 patients with neurodevelopmental disorders diagnosed through postnatal exome sequencing. Causative P/LP nonprotein-truncating and noncanonical splicing variants were retrospectively reclassified in cases without specific prenatal manifestations, disregarding postnatal symptoms. RESULTS: Of the 156 patients, 72 had a nontruncating or noncanonical splicing variant. Six patients were excluded for having more than one possible causative variant. Twelve patients had prenatal malformations known to be associated with the diagnosed disorder; therefore, variant interpretation remained unchanged. In 33 of the 54 remaining cases, the variant had been previously reported as P/LP. Reclassification of the other 21 LP/P variants revealed that 16 would have been classified as VUS if detected prenatally. CONCLUSION: In our cohort, ∼24% (16/66) of causative nonprotein-truncating/noncanonical splicing variants would have been classified as VUS if sequencing had been conducted during pregnancy. The potential for false-negative results, stemming from limitations in the phenotypic information available prenatally, should be discussed with prospective parents. The criteria for classifying and reporting variants in the prenatal setting may require adjustment.

Tumor deposits should not be placed in the M category of TNM: A comparative survival analysis using SEER data.

Tumor deposits (TD) are tumor nodules in the lymphatic drainage area of colorectal cancer patients, and they are currently classified in the N category in the TNM classification. However, due to the associated poor prognosis, some small cohort studies suggest that TD belong in the M category. A retrospective study using The Surveillance, Epidemiology, and End Results program (SEER) data was performed in Stages III and IV colon carcinoma (CC) patients to evaluate the prognostic impact of TD. In multivariate analysis, TD have significantly negative effect on survival in both stages (Stage III HR = 1.4 [95% CI 1.4-1.5] and Stage IV HR = 1.3 [95% CI 1.2-1.3]). In Stage III, 5-year overall survival (OS) for patients with TD 49%, whereas it was 64% for patients without TD (p < .001). Additionally, in Stage IV patients without TD, the 5-year OS rates are superior at 21% compared to patients with TD, who show 5-year OS rate of 10% (p < .001). Stage III patients with TD (5-year OS 49%) have a significantly better prognosis compared to Stage IV patients (5-year OS 17%, p < .001). Therefore, despite the previous suggestions, this large scale study (n = 52,332) on outcomes in CC does not support the classification of TD in Stage IV.

DeepPhoPred: Accurate Deep Learning Model to Predict Microbial Phosphorylation.

Phosphorylation is a substantial posttranslational modification of proteins that refers to adding a phosphate group to the amino acid side chain after translation process in the ribosome. It is vital to coordinate cellular functions, such as regulating metabolism, proliferation, apoptosis, subcellular trafficking, and other crucial physiological processes. Phosphorylation prediction in a microbial organism can assist in understanding pathogenesis and host-pathogen interaction, drug and antibody design, and antimicrobial agent development. Experimental methods for predicting phosphorylation sites are costly, slow, and tedious. Hence low-cost and high-speed computational approaches are highly desirable. This paper presents a new deep learning tool called DeepPhoPred for predicting microbial phospho-serine (pS), phospho-threonine (pT), and phospho-tyrosine (pY) sites. DeepPhoPred incorporates a two-headed convolutional neural network architecture with the squeeze and excitation blocks followed by fully connected layers that jointly learn significant features from the peptide's structural and evolutionary information to predict phosphorylation sites. Our empirical results demonstrate that DeepPhoPred significantly outperforms the existing microbial phosphorylation site predictors with its highly efficient deep-learning architecture. DeepPhoPred as a standalone predictor, all its source codes, and our employed datasets are publicly available at https://github.com/faisalahm3d/DeepPhoPred.

Glioneuronal and neuronal tumors: A perspective.

Glioneuronal and neuronal tumors (GNTs) are slow-growing, lower-grade neuroepithelial tumors characterized by mature neuronal differentiation and, less consistently, glial differentiation. Their identification has traditionally relied on histological proof of neuronal differentiation, reflecting the well-differentiated nature of GNTs. However, after discovering genetic alterations in GNTs, particularly those in the MAP-kinase pathway, it became evident that histological diagnoses do not always correlate with genetic alterations and vice versa. Therefore, molecular-based classification is now warranted since several inhibitors targeting the MAP-kinase pathway are available. The World Health Organization classification published in 2021 applied DNA methylation profiling to segregate low-grade neuroepithelial tumors. As GNTs are essentially indolent, radical resection and unnecessary chemoradiotherapy may be more harmful than beneficial for patients. Preserving tumor tissue for potential future treatments is more important for patients with GNTs.

Associations between fatty infiltration of rotator cuff muscles and tear size or location of rotator cuff tendon.

Background: Fatty infiltration (FI) of rotator cuff muscles in patients with rotator cuff tears is an important imaging factor for determining surgical indications. However, the associations between FI grade and the size or location of adjacent rotator cuff tears are not well-known. This study aimed to primarily determine whether tear size and location, especially for the SSc tendon, are associated with FI of adjacent rotator cuff muscles. The secondary aim was to clarify which patient factors are associated with rotator cuff muscle FI in rotator cuff tear cases. Methods: This study examined 373 shoulders of 348 patients (264 males and 109 females; mean age of 62.8 years) who underwent arthroscopic rotator cuff surgery. The FI grades of the supraspinatus (SSP), infraspinatus (ISP), and subscapularis (SSc) muscles were assessed using preoperative magnetic resonance imaging (MRI) using the Goutallier classification modified by Fuchs. According to the preoperative MRI and intraoperative findings, the tear size of the posterior-superior rotator cuff (SSP-ISP) was classified using a modified six-grade scale of the Cofield classification, and that of the SSc tear was classified using a six-grade scale according to the Lafosse classification. Age at surgery, sex, body mass index (BMI), presence of diabetes mellitus (DM) or hyperlipidemia (HL), trauma history, and duration of symptoms were investigated. Results: The FI grades of the SSP, ISP, and SSc were significantly associated with the size of the tears in those muscles (all P < 0.01). Furthermore, the FI grades of the SSP and the ISP were significantly associated with SSc tear size (P < 0.01), and the FI grade of the SSc was significantly associated with SSP-ISP tear size (P < 0.01). Patient age at surgery was significantly associated with FI grade (P < 0.01), with significant progression of the FI grade with advancing age. However, there were no significant associations between the FI grade and sex, BMI, presence of DM or HL, trauma history, and duration of symptoms. Conclusions: The FI grade of each of the rotator cuff muscles is affected by not only the tear severity of the muscle concerned but also by the severity of any tear in the adjacent rotator cuff.