A weighted Bayesian integration method for predicting drug combination using heterogeneous data.

BACKGROUND: In the management of complex diseases, the strategic adoption of combination therapy has gained considerable prominence. Combination therapy not only holds the potential to enhance treatment efficacy but also to alleviate the side effects caused by excessive use of a single drug. Presently, the exploration of combination therapy encounters significant challenges due to the vast spectrum of potential drug combinations, necessitating the development of efficient screening strategies. METHODS: In this study, we propose a prediction scoring method that integrates heterogeneous data using a weighted Bayesian method for drug combination prediction. Heterogeneous data refers to different types of data related to drugs, such as chemical, pharmacological, and target profiles. By constructing a multiplex drug similarity network, we formulate new features for drug pairs and propose a novel Bayesian-based integration scheme with the introduction of weights to integrate information from various sources. This method yields support strength scores for drug combinations to assess their potential effectiveness. RESULTS: Upon comprehensive comparison with other methods, our method shows superior performance across multiple metrics, including the Area Under the Receiver Operating Characteristic Curve, accuracy, precision, and recall. Furthermore, literature validation shows that many top-ranked drug combinations based on the support strength score, such as goserelin and letrozole, have been experimentally or clinically validated for their effectiveness. CONCLUSIONS: Our findings have significant clinical and practical implications. This new method enhances the performance of drug combination predictions, enabling effective pre-screening for trials and, thereby, benefiting clinical treatments. Future research should focus on developing new methods for application in various scenarios and for integrating diverse data sources.

AB077. An evaluation of prognostic scoring systems for survival in a surgically treated cohort of 318 metastatic spine tumour surgery patients.

BACKGROUND: Survival prognostication plays a key role in the decision-making process for the surgical treatment of patients with spinal metastases. In the past traditional scoring systems such as the modified Tokuhashi and Tomita scoring systems have been used extensively, however in recent years their accuracy has been called into question. This has led to the development of machine learning algorithms to predict survival. In this study, we aim to compare the accuracy of prognostic scoring systems in a surgically treated cohort of patients. METHODS: This is a retrospective review of 318 surgically treated spinal metastases patients between 2009 and 2021. The primary outcome measured was survival from the time of diagnosis. Predicted survival at 3 months, 6 months and 1 year based on the prognostic scoring system was compared to actual survival. Predictive values of each scoring system were measured via area under receiver operating characteristic curves (AUROC). The following scoring systems were compared, Modified Tokuhashi (MT), Tomita (T), Modified Bauer (MB), Van Den Linden (VDL), Oswestry (O), New England Spinal Metastases score (NESMS), Global Spine Study Tumor Group (GSTSG) and Skeletal Oncology Research Group (SORG) scoring systems. RESULTS: For predicting 3 months survival, the GSTSG 0.980 (0.949-1.0) and NESM 0.980 (0.949-1.0) had outstanding predictive value, while the SORG 0.837 (0.751-0.923) and O 0.837 (0.775-0.900) had excellent predictive value. While for 6 months survival, only the O 0.819 (0.758-0.880) had excellent predictive value and the GSTSG 0.791(0.725-0.857) had acceptable predictive value. For 1 year survival, the NESM 0.871 (0.822-0.919) had excellent predictive value and the O 0.722 (0.657-0.786) had acceptable predictive value. The MT, T and MB scores had an area under the curve (AUC) of <0.5 for 3-month, 6-month and 1-year survival. CONCLUSIONS: Increasingly, traditional scoring systems such as the MT, T and MB scoring systems have become less predictive. While newer scoring systems such as the GSTSG, NESM and SORG have outstanding to excellent predictive value, there is no one survival scoring system that is able to accurately prognosticate survival at all 3 time points. A multidisciplinary, personalised approach to survival prognostication is needed.

Sepsis in obstetric care for the emergency clinician: A review.

Sepsis remains a leading cause of mortality among pregnant and recently pregnant patients, rendering it a subject of vital importance to emergency clinicians in the US. However, death by sepsis has been found to be largely preventable with prompt and appropriate intervention. This narrative review provides a summary of the physiologic, epidemiologic, and systemic factors specific to obstetric sepsis that contribute to delays in diagnosis and treatment. Additionally, it provides a framework for emergency department providers to approach infection identification, antimicrobial selection, and appropriate resuscitation prior to disposition.

Development of a machine learning-based target-specific scoring function for structure-based binding affinity prediction for human dihydroorotate dehydrogenase inhibitors.

Human dihydroorotate dehydrogenase (hDHODH) is a flavin mononucleotide-dependent enzyme that can limit de novo pyrimidine synthesis, making it a therapeutic target for diseases such as autoimmune disorders and cancer. In this study, using the docking structures of complexes generated by AutoDock Vina, we integrate interaction features and ligand features, and employ support vector regression to develop a target-specific scoring function for hDHODH (TSSF-hDHODH). The Pearson correlation coefficient values of TSSF-hDHODH in the cross-validation and external validation are 0.86 and 0.74, respectively, both of which are far superior to those of classic scoring function AutoDock Vina and random forest (RF) based generic scoring function RF-Score. TSSF-hDHODH is further used for the virtual screening of potential inhibitors in the FDA-Approved & Pharmacopeia Drug Library. In conjunction with the results from molecular dynamics simulations, crizotinib is identified as a candidate for subsequent structural optimization. This study can be useful for the discovery of hDHODH inhibitors and the development of scoring functions for additional targets.

Hypoxic-ischemic brain injury in pig after cardiac arrest - A new histopathological scoring system for non-specialists.

Introduction: After cardiac arrest and successful resuscitation patients often present with hypoxic-ischemic brain injury, which is a major cause of death due to poor neurological outcome. The development of a robust histopathological scoring system for the reliable and easy identification and quantification of hypoxic-ischemic brain injury could lead to a standardization in the evaluation of brain damage. We wanted to establish an easy-to-use neuropathological scoring system to identify and quantify hypoxic-ischemic brain injury. Methods: The criteria for regular neurons, hypoxic-ischemic brain injury neurons and neurons with ischemic neuronal change (ischemic change neurons) were established in collaboration with specialized neuropathologists. Nine non-specialist examiners performed cell counting using the mentioned criteria in brain tissue samples from a porcine cardiac arrest model. The statistical analyses were performed using the interclass correlation coefficient for counting data and reliability testing. Results: The inter-rater reliability for regular neurons (ICC 0.68 (0.42 - 0.84; p < 0.001) and hypoxic-ischemic brain injury neurons (ICC 0.87 (0.81 - 0.92; p < 0.001) showed moderate to excellent correlation while ischemic change neurons showed poor reliability. Excellent results were seen for intra-rater reliability for regular neurons (ICC 0.9 (0.68 - 0.97; p < 0.001) and hypoxic-ischemic brain injury neurons (ICC 0.99 (0.83 - 1; p < 0.001). Conclusion: The scoring system provides a reliable method for the discrimination between regular neurons and neurons affected by hypoxic/ischemic injury. This scoring system allows an easy and reliable identification and quantification of hypoxic-ischemic brain injury for non-specialists and offers a standardization to evaluate hypoxic-ischemic brain injury after cardiac arrest.

Natural Frequencies Improve Public Understanding of Medical Test Results: An Experimental Study on Various Bayesian Inference Tasks with Multiple Scoring Methods and Non-Bayesian Reasoning Strategies.

BACKGROUND: It is well established that the natural frequencies (NF) format is cognitively more beneficial for Bayesian inference than the conditional probabilities (CP) format. However, empirical studies have suggested that the NF facilitation effect might be limited to specific groups of individuals. Unlike previous studies that focused on a limited number of Bayesian inference problems evaluated by a single scoring method, it was essential to examine multiple Bayesian problems using various scoring metrics. This study also explored the impact of numeracy on Bayesian inference and assessed non-Bayesian cognitive strategies using the numerical information in problem solving. METHODS: In a Web-based experimental survey, 175 South Korean adults were randomly assigned to 1 of 2 format groups (NF v. CP). After completing numeracy scales, participants were asked to estimate 4 Bayesian inference problems and document the numerical information used in their problem-solving process. Four scoring methods-strict rounding, loose rounding, absolute deviation, and 50-Split-were used to evaluate participants' estimations. RESULTS: The NF format generally outperformed the CP format across all problems, except in a chorionic villus sampling test problem when evaluated using the 50-Split method. In addition, numeracy levels significantly influenced Bayesian inference; participants with higher numeracy demonstrated better performance. In addition, participants used various non-Bayesian strategies influenced by the format and the nature of the problems. CONCLUSIONS: The NF facilitation effect was consistently observed across multiple Bayesian problems and scoring methods. Individuals with higher numeracy levels benefited more from the NF format. The use of various non-Bayesian strategies varied with the formats and nature of specific tasks. HIGHLIGHTS: The natural frequencies (NF) format is known to foster understanding of medical test results compared with the conditional probabilities (CP) format, but some studies have reported that this benefit is either nonexistent or limited to specific groups.This study aims to replicate previous empirical studies using various Bayesian problems using multiple scoring methods.The NF format fosters understanding of medical test results across all Bayesian problems by all scoring methods, except in the CVS problem when using a 50-Split scoring method.Participants with high numeracy perform better Bayesian inference than those with lower numeracy. Particularly, higher numerates benefit more in the NF format than lower numerates do. In addition, the public tend to use various non-Bayesian reasoning strategies depending on the format and the nature of the tasks.

Ultrasound assessment and scoring of tendinopathy in hemophilia: Development of the Tendon Haemophilic Damage - Ultrasound (THD-US) method.

This study aimed to develop and validate the tendinopathy hemophilia detection with ultrasonography (THD-US) protocol for assessing hemophilia-related tendinopathy. Twenty male patients with hemophilic arthropathy underwent ultrasound evaluations of 200 tendons. The THD-US scoring method assessed structural changes, hyperemia, and calcifications, revealing various tendon abnormalities. This protocol provides a standardized, efficient method for assessing tendinopathy in hemophilia patients, potentially improving patient management and outcomes.

Evaluation of outcomes of clinical phenotyping-based treatment for bladder pain syndrome/interstitial cystitis.

Introduction: Bladder pain syndrome/Interstitial cystitis (BPS/IC) is clinically of diverse types because different causes contribute to the development of their symptoms. It is important to classify patients into various groups based on the possible etiopathogenesis of their condition. Treatment may be tailored to each specific group according to the possible cause. Methodology: Twenty-five patients diagnosed with BPS/IC were categorized into four different clinical phenotypes (CP) based on their history of symptoms, allergy, dysfunctional voiding, neuropathic pain, and the presence of Hunner's ulcer. Some patients could be classified into multiple groups. The patients were given oral pentosan polysulfate, and treatment specific to their CP. Patients in CP1, CP2, and CP3 groups received, respectively hydroxyzine, clonazepam, and amitriptyline. Patients with Hunner's lesions (HL) (CP4) underwent hydro distension and ablation of the lesion, followed by intravesical instillation of heparin and hydrocortisone. The patients were evaluated using the Apollo clinical scoring (ACS) system and their clinical scores were recorded at 1, 3, and 6 month(s). Results: Among the 25 patients, 5, 7, 4, and 9 patients were classified into CP 1 - CP4 groups respectively, and were all subjected to ACS assessment. In CP1 group (allergy group), 80% (4/5) of patients responded well to the treatment and 20% (1/5) had unsatisfactory responses. In CP2 group (dysfunctional voiding group), 71.42% (5/7) patients had good, and 28.57% (2/7) had excellent responses. In CP3 group (neuropathic pain group), 28.57% (3/4) patients had excellent, and 75% (1/4) patients had good responses. In CP4 group (HL group), 33.33% (3/9) patients had unsatisfactory, 44.44% (4/9) achieved good, and 22.22% (2/9) had excellent responses. Overall, 16% (4/25) patients had unsatisfactory, 56% (14/25) attained good, and 28% (7/25) had an excellent response at the completion of the study. Conclusion: Using clinical phenotyping-based features indicative of etiology could potentially improve treatment outcomes by targeting the specific pathological processes contributing to the patients' symptoms.

"Predicting diabetic kidney disease in youth with type 1 diabetes: Insights from genetic risk assessment".

OBJECTIVE: Diabetic kidney disease (DKD) is influenced by multiple factors, yet its precise progression mechanisms remain largely unclear. This study aimed to create a clinical risk-scoring system based on genetic polymorphisms in the AFF3, CARS, CERS2, ERBB4, GLRA3, RAET1L, TMPO, and ZMIZ1 genes. METHODS: The study included a DKD group diagnosed with diabetic kidney disease before age 18 and a WDC group matched by age, gender, and age at diabetes diagnosis. Genetic data and clinical data from diabetes diagnosis to moderately increased albuminuria (MIA) detection were compared between the groups. RESULTS: Among 43 DKD cases, 22 were girls and 21 were boys. At MIA diagnosis, mean body weight SDS was -0.24 ± 0.94, height SDS was 0.34 ± 1.15, and BMI SDS was -0.26 ± 0.94. Systolic blood pressure was at the 72nd percentile (2-99), and diastolic blood pressure was at the 74th percentile (33-99). Significant differences in rs267734, rs267738, and rs942263 polymorphisms were found between DKD and non-complication diabetic groups (13[30.2 %] vs 5[11.6 %], p = 0.034; 14[32.6 %] vs 5[11.6 %], p = 0.019; 26[60.5 %] vs 40[93 %], p < 0.001). CONCLUSION: Several factors were identified as significant in DKD onset, including low follow-up weight SDS, elevated diastolic blood pressure, presence of rs267734, and absence of rs942263 polymorphisms. The model demonstrated a specificity of 81.4 % and a sensitivity of 74.4 %.

Utilizing Modified Coopland's Scoring System to Identify and Predict the Outcome of High-Risk Pregnancies in Resource-Limited Settings: A Retrospective Review.

Introduction A high-risk pregnancy is associated with adverse maternal and foetal outcomes. Women with high-risk pregnancies are at a greater risk of developing antepartum haemorrhage, miscarriages, and the need for surgical interventions. Neonatal complications include preterm births, low birth weight (LBW), intra-uterine deaths and an increased need for NICU admission. The utilisation of low-cost scoring tools for identifying high-risk women can aid in early diagnosis and timely implementation of therapeutic interventions.  Objective The retrospective record-based study sought to calculate the proportion of high-risk pregnancies using modified Coopland's scoring system and compare the maternal and foetal outcomes among high-risk pregnancies. Methods The study retrospectively analysed the records of antenatal women in their third trimester from the years December 2018 to December 2021. Each record was then numerically assessed according to the modified Coopland's scoring system and categorised according to the risk status. Maternal and neonatal outcomes were then compared across the risk groups. Results The data included 300 cases over a three-year period. According to modified Coopland's scoring system, we found that the overall proportion of high-risk pregnancies was 18.3%. Adverse maternal and fetal outcomes were increased in high-risk pregnancy groups when compared to low-risk pregnancies, miscarriages (31.6% vs 15.8%) and antepartum haemorrhage (55.6% vs 11.1%). Babies born to high-risk mothers had a higher chance of developing LBW status (52.0%) and respiratory distress (45.5%) when compared to those born to low-risk mothers: 8.0% and 13.6%, respectively. Conclusion A notable portion of pregnant women were classified as high-risk using modified Coopland's scoring tool and would benefit from targeted obstetric care.

Enhancing protein-ligand binding affinity prediction through sequential fusion of graph and convolutional neural networks.

Predicting protein-ligand binding affinity is a crucial and challenging task in structure-based drug discovery. With the accumulation of complex structures and binding affinity data, various machine-learning scoring functions, particularly those based on deep learning, have been developed for this task, exhibiting superiority over their traditional counterparts. A fusion model sequentially connecting a graph neural network (GNN) and a convolutional neural network (CNN) to predict protein-ligand binding affinity is proposed in this work. In this model, the intermediate outputs of the GNN layers, as supplementary descriptors of atomic chemical environments at different levels, are concatenated with the input features of CNN. The model demonstrates a noticeable improvement in performance on CASF-2016 benchmark compared to its constituent CNN models. The generalization ability of the model is evaluated by setting a series of thresholds for ligand extended-connectivity fingerprint similarity or protein sequence similarity between the training and test sets. Masking experiment reveals that model can capture key interaction regions. Furthermore, the fusion model is applied to a virtual screening task for a novel target, PI5P4Kα. The fusion strategy significantly improves the ability of the constituent CNN model to identify active compounds. This work offers a novel approach to enhancing the accuracy of deep learning models in predicting binding affinity through fusion strategies.

The scoring system combined with radiomics and imaging features in predicting the malignant potential of incidental indeterminate small (<20 mm) solid pulmonary nodules.

OBJECTIVE: Develop a practical scoring system based on radiomics and imaging features, for predicting the malignant potential of incidental indeterminate small solid pulmonary nodules (IISSPNs) smaller than 20 mm. METHODS: A total of 360 patients with malignant IISSPNs (n = 213) and benign IISSPNs (n = 147) confirmed after surgery were retrospectively analyzed. The whole cohort was randomly divided into training and validation groups at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used to debase the dimensions of radiomics features. Multivariate logistic analysis was performed to establish models. The receiver operating characteristic (ROC) curve, area under the curve (AUC), 95% confidence interval (CI), sensitivity and specificity of each model were recorded. Scoring system based on odds ratio was developed. RESULTS: Three radiomics features were selected for further model establishment. After multivariate logistic analysis, the combined model including Mean, age, emphysema, lobulated and size, reached highest AUC of 0.877 (95%CI: 0.830-0.915), accuracy rate of 83.3%, sensitivity of 85.3% and specificity of 80.2% in the training group, followed by radiomics model (AUC: 0.804) and imaging model (AUC: 0.773). A scoring system with a cutoff value greater than 4 points was developed. If the score was larger than 8 points, the possibility of diagnosing malignant IISSPNs could reach at least 92.7%. CONCLUSION: The combined model demonstrated good diagnostic performance in predicting the malignant potential of IISSPNs. A perfect accuracy rate of 100% can be achieved with a score exceeding 12 points in the user-friendly scoring system.

Internal validation of modified Mirels' scoring system for pathologic femur fractures.

BACKGROUND: The proximal femur is a common site of bone metastasis. The Mirels' score is a frequently utilized system to identify patients at risk for pathologic fracture and while it has consistently demonstrated strong sensitivity, specificity has been relatively poor. Our group previously developed a Modified Mirels' scoring system which demonstrated improved ability to predict cases at risk of fracture in this patient population through modification of the Mirels' location score. The purpose of the present study is to internally validate this newly developed scoring system on an independent patient series. METHODS: Retrospective review was performed to identify patients who were evaluated for proximal femoral bone lesions. Patients were stratified into one of two groups: 1) those who went on to fracture within 4 months after initial evaluation (Fracture Group) and 2) those who did not fracture within 4 months of initial evaluation (No Fracture Group). Retrospective chart review was performed to assign an Original Mirels' (OM) Score and Modified Mirels' (MM) score to each patient at the time of initial evaluation. Descriptive statistics, logistic regression, receiver operating curve, and net benefit analyses were performed to determine the predictability of fractures when utilizing both scoring systems. RESULTS: The use of the MM scoring improved fracture prediction over OM scoring for patients observed over a 4 month follow up based on logistic regression. Decision curve analysis showed that there was a net benefit using the MM score over the OM scoring for a full range of fracture threshold probabilities. Fracture prevalence was similar for current internal validation dataset when compared to the dataset of our index study with a comparable reduction in misclassification of fracture prediction when utilizing the modified scoring system versus the original. CONCLUSIONS: Use of MM scoring was found to improve fracture prediction over OM scoring when tested on an internal validation set of patients with disseminated metastatic lesions to the proximal femur. The improvement in fracture prediction demonstrated in the present study mirrored the results of our index study during which the MM system was developed.

Correlation between body mass index and functional outcome of total knee replacement for osteoarthritis of knee- a prospective study on South Indian cohort.

Background: Over the past 20 years, there has been an increase in demand for complete knee replacements, and this trend is predicted to continue. It has been shown that being overweight is a risk factor for knee osteoarthritis. There are only a few studies on this in India and none on South Indian patients, Therefore, our goal was to evaluate how BMI affected functional outcomes after primary total knee replacement. Objectives: To determine the impact of body mass index (BMI) on functional outcomes after primary total knee replacement. Method: ology: Patients who underwent total knee replacement between November 2021 and November 2023 were included in the study. Patients were divided into groups based on BMI. Group I patients have a BMI less than 25, and group II patients of BMI greater than 25. International Knee Society scoring(IKSS) is used to assess patients Preoperatively and postoperatively. Results: Out of 185 patients, 70 were males and 115 were females. When IKSS scores were analyzed the mean Knee score before surgery in Group 1 was 24.58 and in Group 2 it was 16.64. After 1 year follow up the mean scores were 68.5 and 57.5 respectively. When analyzed with functional score the pre-op scores for groups 1 and 2 were 32.58 and 23.44 respectively and post-op scores after one-year follow-up were 71.17 and 51.7 respectively. Conclusion: BMI does have a positive correlation with both preoperative and postoperative scores. A weight-loss programme can be discussed with the patients presenting the results of this study.

Eat, Sleep, Console model for neonatal opioid withdrawal syndrome: a meta-analysis.

Background: The rising incidence of drug abuse among pregnant women has rendered neonatal opioid withdrawal syndrome a significant global health concern. Methods: Databases including PubMed, Web of Science, the Cochrane Library, Embase, Elton B. Stephens. Company (EBSCO), China National Knowledge Infrastructure (CNKI), and Wanfang were searched for comparative studies of the Eat, Sleep, Console model vs. traditional assessment tools for neonatal opioid withdrawal syndrome. Two reviewers conducted literature searches, screened according to the inclusion criteria, extracted data, and independently verified accuracy. All meta-analyses were conducted using Review Manager Version 5.4. Results: In total, 18 studies involving 4,639 neonates were included in the meta-analysis. The Eat, Sleep, Console model demonstrated superior outcomes in assessing neonatal opioid withdrawal syndrome, significantly reducing the need for pharmacological treatment [risk ratio = 0.44, 95% confidence interval (CI) = 0.34-0.56, P < 0.001], decreasing the length of hospital stay [standard mean difference (SMD) = -2.10, 95% CI = -3.43 to -0.78, P = 0.002], and shortening the duration of opioid treatment (SMD = -1.33, 95% CI = -2.22 to -0.45, P = 0.003) compared to the Finnegan Neonatal Abstinence Scoring System. Conclusions: The Eat, Sleep, Console model is more effective than the Finnegan Neonatal Abstinence Scoring System in improving the assessment and management of neonatal opioid withdrawal syndrome.

Development and validation of a predictive scoring model for risk stratification of tuberculosis treatment interruption.

BACKGROUND: Tuberculosis (TB) treatment interruption poses risks of antimicrobial resistance, potentially leading to treatment failure and mortality. Addressing the risk of early treatment interruption is crucial in tuberculosis care and management to improve treatment outcomes and curb disease transmission. OBJECTIVES: This study aimed to identify risk factors of TB treatment interruption and construct a predictive scoring model that enables objective risk stratification for better prediction of treatment interruption. METHODS: A multicentre retrospective cohort study was conducted at public health clinics in Sarawak, Malaysia over 11 months from March 2022 to January 2023, involving adult patients aged ≥18 years with drug-susceptible TB diagnosed between 2018 and 2021. Cumulative missed doses or discontinuation of TB medications for ≥2 weeks, either consecutive or non-consecutive, was considered as treatment interruption. The model was developed and internally validated using the split-sample method. Multiple logistic regression analysed 18 pre-defined variables to identify the predictors of TB treatment interruption. The Hosmer-Lemeshow test and area under the receiver operating characteristic curve (AUC) were employed to evaluate model performance. RESULTS: Of 2953 cases, two-thirds (1969) were assigned to the derivation cohort, and one-third (984) formed the validation cohort. Positive predictors included smoking, previously treated cases, and adverse drug reactions, while concurrent diabetes was protective. Based on the validation dataset, the model demonstrated good calibration (P = 0.143) with acceptable discriminative ability (AUC = 0.775). A cutoff score of 2.5 out of 11 achieved a sensitivity of 81 % and a specificity of 64.4 %. Risk stratification into low (0-2), medium (3-5), and high-risk (≥6) categories showed ascending interruption rates of 5.3 %, 18.1 %, and 41.3 %, respectively (P < 0.001). CONCLUSION: The predictive scoring model aids in risk assessment for TB treatment interruption, enabling focused monitoring and personalized intervention plans for higher-risk groups in the early treatment phase.

Prediction Models for HIV Infection in Infants: Analysis of Scoring Systems on Maternal, Infants, and Mode of Delivery Risk Factors.

BACKGROUND: Diagnosis for HIV in infants is hard to determine, particularly in limited- resource areas. A delay in the diagnosis of HIV-infected infants will lead to high morbidity and mortality. The purpose of this project is to construct a model of an HIV-positive infant and develop a useful and practical scoring system to estimate the likelihood of mother-to-child transmission that can be applied in the field. METHODS: A cross-sectional study on 100 subjects through medical records of infants born to HIV-infected mothers was conducted at four hospitals and one community health center. Several models of risk prediction scores of HIV-infected infants were then made. Furthermore, the performed validation was performed on 20 subjects of infants born to mothers with HIV in three hospitals by comparing the scoring system and the result of the PCR RNA examination performed at the age of 6 weeks old. RESULTS: The risk of HIV-infected infants was higher in mothers who did not receive ARV through PMTCT programs (OR 33.6; 95% CI 4.0 to 282.2), pulmonary TB infection (OR 5.1; IK95% 1.6 to 16.0) and vaginal delivery (OR 9.2; IK95 2.2 to 38.0%). Two models can predict the occurrence of infected HIV infants effectively. Model 1 consists of maternal age, maternal ARVs, lung TB infection, gestational age, mode of delivery, and sex of the infants with sensitivity and specificity of 78.9% and 70.8% (AUC=0.817 [95% CI 0.709 to 0.926]) and likelihood ratio score of 4. Model 2 consists of ARVs to the mother, pulmonary TB infection, and mode of delivery with sensitivity and specificity of 73.7% and 86.1%; AUC value of 0.812 (95% CI 0.687 to 0.938) and likelihood ratio of 5. External Validation gave similar results to the Model 2 scoring system with PCR RNA. CONCLUSION: The prediction score of HIV-infected infants in Model 2 can be used in newborns of HIV-positive mothers as an effective and practical risk screening tool for HIV-infected infants before the gold standard examination by PCR.

Predicting Inhibition of CDK2 with SAnDReS: The Application of Machine Learning to Navigate the Scoring Function Space.

CDK2 plays a pivotal role in controlling the progression of the cell cycle and is a target for anticancer drugs. The last 30 years of structural studies focused on CDK2 provided the basis for understanding its inhibition and furnished the data to develop machine-learning models to study intermolecular interactions. This review addresses the application of computational models to estimate the inhibition of CDK2. It focuses on machine-learning models developed to predict binding affinity against CDK2 using the program SAnDReS. A search of previously published articles on PubMed showed machine-learning models built to evaluate CDK2 inhibition. BindingDB information for CDK2 furnished the data to generate updated machine-learning models to predict the inhibition of this enzyme. The application of SAnDReS to model CDK2-inhibitor interactions showed that this approach can build machine-learning models with superior predictive performance compared with classical and deep-learning scoring functions. Also, the innovative DOME analysis of the predictive performance of machine learning and universal scoring function indicates that this method is adequate to select computational models to address protein-ligand interactions. The available structural and functional data about CDK2 is a rich source of information to build machine-learning models to predict the inhibition of this protein target. SAnDReS can build superior models to predict pKi and outperform universal scoring functions, including one developed using deep learning.

Emotion word repertoire in the adult attachment interview is linked to reduced suicide attempts in patients with borderline personality disorder.

Background: This study explored the association between emotion word repertoire (EWR), attachment, reflective functioning and personality organization (PO) and suicidal behavior in borderline personality disorder (BPD) patients. Methods: The current study performed a secondary data analysis from a randomized control trial for BPD patients (all female; n = 87; age: m = 27; SD = 7.42). EWR was assessed via machine-scoring transcripts of Adult Attachment Interviews (AAI) for affective words using the VETA electronic scoring software for the Levels of Emotional Awareness Scale (LEAS). Generated scores were related to impairments in PO (Structured Interview for Personality Organization; STIPO), attachment organization (AAI) and mentalization (Reflective Functioning Scale), general symptom severity (Brief Symptom Inventory; BSI-53), self-harm and suicidal behavior. Independent effects of the investigated predictors were studied using Bayesian path analysis. Results: Corrected for education, findings in Bayesian path analysis suggest an independent negative association between EWR and suicide attempts (BE = -.32; 95 % CI [-.51, -.12]) and positive associations of deficits in PO with psychiatric symptoms (BE = .23; 95 % CI [.01, .44]) as well as suicide attempts (BE = .30; 95 % CI [.08, .49]). Discussion: The findings underscore the potential role of high EWR and PO as a protective factor for suicidal behavior in individuals with BPD.