Chronic toxicity of sediment-bound triclosan on freshwater walking catfish Clarias magur: Organ level accumulation and selected enzyme biomarker responses.

Triclosan (TCS) is a biocide widely used in personal care and medicinal products. TCS persists in sediments and has been detected worldwide, making sediments a vital route of TCS exposure to aquatic organisms. This experiment explored the bioaccumulation and toxicological effects of TCS-contaminated sediment. The study revealed that the half-life of TCS in the sediment-water system was 21.52 days. Exposure of Clarias magur juveniles to 0.4 and 0.8 mg kg-1 TCS-spiked sediment resulted in high Biota-Sediment Accumulation Factor (BSAF) with the highest bioaccumulation in the liver (29.62-73.61 mg kg-1), followed by gill (9.22-17.57 mg kg-1), kidney (5.04-9.76 mg kg-1), muscle (2.63-4.87 mg kg-1) and brain (1.53-3.20 mg kg-1). Furthermore, a concentration-dependent increase in oxidative stress biomarkers such as superoxide dismutase (SOD), catalase (CAT) and glutathione-S-transferase (GST) was documented during 45 days of exposure in gill, liver, kidney, muscle, and brain tissues of exposed fish. A similar increasing trend was also recorded for liver transaminase enzymes such as glutamic oxaloacetic transaminase (GOT) and glutamic pyruvic transaminase (GPT) during the experimental period compared to control. Serum biochemical analysis revealed a significant time and concentration-dependent increase in serum glucose, serum GOT, and serum GPT, while serum total protein and albumin decreased significantly during exposure. These findings demonstrate high bioaccumulative and toxic nature of TCS in fish, promoting multiple physiological and biochemical dysfunctions through sediment exposure. The study underscores the urgent need for strengthened regulations and robust monitoring of triclosan across various environmental matrices, including sediment, to mitigate the detrimental impacts of TCS effectively.

PRRT with Lu-177 DOTATATE in Treatment-Refractory Progressive Meningioma: Initial Experience from a Tertiary-Care Neuro-Oncology Center.

PURPOSE: Refractory and/or recurrent meningiomas have poor outcomes, and the treatment options are limited. Peptide receptor radionuclide therapy (PRRT) has been used in this setting with promising results. We have documented our experience of using intravenous (IV) and intra-arterial (IA) approaches of Lu-177 DOTATATE PRRT. METHODS: Eight patients with relapsed/refractory high-grade meningioma received PRRT with Lu-177 DOTATATE by IV and an IA route. At least 2 cycles were administered. Time to progression was calculated from the first PRRT session to progression. The response was assessed on MRI using RANO criteria, and visual analysis of uptake was done on Ga-68 DOTANOC PET/CT. Post-therapy dosimetry calculations for estimating the absorbed dose were performed. RESULTS: Median time to progression was 8.9 months. One patient showed disease progression, whereas seven patients showed stable disease at 4 weeks following 2 cycles of PRRT. Dosimetric analysis showed higher dose and retention time by IA approach. No significant peri-procedural or PRRT associated toxicity was seen. CONCLUSION: PRRT is a safe and effective therapeutic option for relapsed/refractory meningioma. The IA approach yields better dose delivery and should be routinely practised.

Heavy metals and trace minerals in commonly available shark species from North East Arabian Sea: A human health risk perspective.

Shark is a seafood commodity that is a good source of minerals and accumulates heavy metals and trace elements through biomagnification, which can pose health risk if taken above the permissible limit. A study was conducted on commonly landed eleven shark species (Scoliodon laticaudus, Rhizopriodon oligolinx, Sphyrna lewini (CR), Carcharhinus macloti, Carcharinus limbatus, Carcharhinus amblyrhynchoides, Carcharhinus sorrah, Carcharinus falciformes(VU), Glaucostegus granulatus, Chiloscyllium arabicum, Loxodon macrorhinus) and analyzed for their heavy metal content, Hazard Index, Total Hazard Quotient, Metal Pollution Index, and also calculated the health risk associated with the consumption. Most of the heavy metals and trace minerals were found to be within the acceptable limit. The Targeted Hazard Quotient (THQ) and the Hazard Index (HI) of all the species except two were less than 1 (HI ≤ 1.0). The Metal Pollution Index (MPI) is showing either no impact or very low contamination. An overall study on hazard identification and health risk characterization in terms of heavy metals shows contamination of some heavy metals in sharks, but there is no potential human health risk associated with consumption.

A Randomized Comparative Study of the Use of Individual Modality and Combination of Endoscopic Retrograde Cholangiopancreatography (ERCP) and Digital Single-Operator Cholangioscopy (DSOC) for Diagnosis of Indeterminate Biliary Strictures.

Background: To determine the superiority of the combination of endoscopic retrograde cholangiopancreatography (ERCP) and digital single-operator cholangioscopy (DSOC) in the same sitting over the individual modality alone in patients with indeterminate biliary strictures. Materials and Methods: A randomized study enrolled 60 adult patients with biliary strictures who were randomized into two groups: ERCP + DSOC and ERCP/DSOC. Histopathologic or cytologic assessment was performed in terms of benign, indeterminate, or malignant nature of the strictures. Procedural adverse events were documented. Accuracy in terms of sensitivity (Sn), specificity (Sp), and predictive value [positive (PPV) and negative (NPV)] were noted. Results: As per final diagnosis, in ERCP/DSOC group, there were 12 (40%) benign cases and 18 (60%) malignant cases, and in group ERCP + DSOC, there were 8 (26.67%) benign cases and 22 (73.33%) malignant cases. ERCP/DSOC labeled 16 (53.33%) patients as benign, 8 (26.67%) as malignant, and 6 (20%) as indeterminate, while ERCP + DSOC labeled 8 (26.67%) as benign, 17 (56.67%) as malignant, and 5 (16.67%) as indeterminate. The Sn, Sp, PPV, and NPV of ERCP/DSOC were 44.4%, 75%, 100%, and 56.25%, and for ERCP + DSOC was 77.27%, 62.50%, 100%, and 62.5%, respectively (P = 0.033). Side effects were statistically similar in both the groups (P > 0.05). Conclusion: To conclude, the combination of ERCP with DSOC is safe and effective with higher diagnostic sensitivity (77.27%) in comparison to standard ERCP or DSOC alone (44.4%) for the diagnosis of biliary strictures.

Impact of extreme weather events on healthcare utilization and mortality in the United States.

Climate change is intensifying extreme weather events. Yet a systematic analysis of post-disaster healthcare utilization and outcomes for severe weather and climate disasters, as tracked by the US government, is lacking. Following exposure to 42 US billion-dollar weather disasters (severe storm, flood, flood/severe storm, tropical cyclone and winter storm) between 2011 and 2016, we used a difference-in-differences (DID) approach to quantify changes in the rates of emergency department (ED) visits, nonelective hospitalizations and mortality between fee-for-service Medicare beneficiaries in affected compared to matched control counties in post-disaster weeks 1, 1-2 and 3-6. Overall, disasters were associated with higher rates of ED utilization in affected counties in post-disaster week 1 (DID of 1.22% (95% CI, 0.20% to 2.25%; P < 0.020)) through week 2. Nonelective hospitalizations were unchanged. Mortality was higher in affected counties in week 1 (DID of 1.40% (95% CI, 0.08% to 2.74%; P = 0.037)) and persisted for 6 weeks. Counties with the greatest loss and damage experienced greater increases in ED and mortality rates compared to all affected counties. Thus, billion-dollar weather disasters are associated with excess ED visits and mortality in Medicare beneficiaries. Tracking these outcomes is important for adaptation that protects patients and communities, health system resilience and policy.

Comparing the performance of a deep learning-based lung gross tumour volume segmentation algorithm before and after transfer learning in a new hospital.

Objectives: Radiation therapy for lung cancer requires a gross tumour volume (GTV) to be carefully outlined by a skilled radiation oncologist (RO) to accurately pinpoint high radiation dose to a malignant mass while simultaneously minimizing radiation damage to adjacent normal tissues. This is manually intensive and tedious however, it is feasible to train a deep learning (DL) neural network that could assist ROs to delineate the GTV. However, DL trained on large openly accessible data sets might not perform well when applied to a superficially similar task but in a different clinical setting. In this work, we tested the performance of DL automatic lung GTV segmentation model trained on open-access Dutch data when used on Indian patients from a large public tertiary hospital, and hypothesized that generic DL performance could be improved for a specific local clinical context, by means of modest transfer-learning on a small representative local subset. Methods: X-ray computed tomography (CT) series in a public data set called "NSCLC-Radiomics" from The Cancer Imaging Archive was first used to train a DL-based lung GTV segmentation model (Model 1). Its performance was assessed using a different open access data set (Interobserver1) of Dutch subjects plus a private Indian data set from a local tertiary hospital (Test Set 2). Another Indian data set (Retrain Set 1) was used to fine-tune the former DL model using a transfer learning method. The Indian data sets were taken from CT of a hybrid scanner based in nuclear medicine, but the GTV was drawn by skilled Indian ROs. The final (after fine-tuning) model (Model 2) was then re-evaluated in "Interobserver1" and "Test Set 2." Dice similarity coefficient (DSC), precision, and recall were used as geometric segmentation performance metrics. Results: Model 1 trained exclusively on Dutch scans showed a significant fall in performance when tested on "Test Set 2." However, the DSC of Model 2 recovered by 14 percentage points when evaluated in the same test set. Precision and recall showed a similar rebound of performance after transfer learning, in spite of using a comparatively small sample size. The performance of both models, before and after the fine-tuning, did not significantly change the segmentation performance in "Interobserver1." Conclusions: A large public open-access data set was used to train a generic DL model for lung GTV segmentation, but this did not perform well initially in the Indian clinical context. Using transfer learning methods, it was feasible to efficiently and easily fine-tune the generic model using only a small number of local examples from the Indian hospital. This led to a recovery of some of the geometric segmentation performance, but the tuning did not appear to affect the performance of the model in another open-access data set. Advances in knowledge: Caution is needed when using models trained on large volumes of international data in a local clinical setting, even when that training data set is of good quality. Minor differences in scan acquisition and clinician delineation preferences may result in an apparent drop in performance. However, DL models have the advantage of being efficiently "adapted" from a generic to a locally specific context, with only a small amount of fine-tuning by means of transfer learning on a small local institutional data set.

[90Y]Yttria Alumino Silicate Glass Microspheres: A Biosimilar Formulation to "TheraSphere" for Cost-Effective Treatment of Liver Cancer.

Background: Selective internal radiation therapy (SIRT) using a suitable ß--emitting radionuclide is a promising treatment modality for unresectable liver carcinoma. Yttrium-90 (90Y) [T1/2 = 64.2 h, Eß(max) = 2.28 MeV, no detectable γ-photon] is the most preferred radioisotope for SIRT owing to its favorable decay characteristics. Objective: The present study describes indigenous development and evaluation of intrinsically radiolabeled [90Y]yttria alumino silicate ([90Y]YAS) glass microsphere, a formulation biosimilar to "TheraSphere" (commercially available, U.S. FDA-approved formulation), for SIRT of unresectable liver carcinoma in human patients. Methods: YAS glass microspheres of composition 40Y2O3-20Al2O3-40SiO2 (w/w) and diameter ranging between 20 and 36 µm were synthesized with almost 100% conversion efficiency and >99% sphericity. Intrinsically labeled [90Y]YAS glass microspheres were produced by thermal neutron irradiation of cold YAS glass microspheres in a research reactor. Subsequent to in vitro evaluations and in vivo studies in healthy Wistar rats, customized doses of [90Y]YAS glass microspheres were administered in human patients. Results: [90Y]YAS glass microspheres were produced with 137.7 ± 8.6 MBq/mg YAS glass (∼6800 Bq per microsphere) specific activity and 99.94% ± 0.02% radionuclidic purity at the end of irradiation. The formulation exhibited excellent in vitro stability in human serum and showed >97% retention in the liver up to 7 d post-administration when biodistribution studies were carried out in healthy Wistar rats. Yttrium-90 positron emission tomography scans recorded at different time points post-administration of customized dose of [90Y]YAS glass microspheres in human patients showed near-quantitative retention of the formulation in the injected lobe. Conclusions: The study confirmed the suitability of indigenously prepared [90Y]YAS glass microspheres for clinical use in the treatment of unresectable hepatocellular carcinoma.

Lessons From the Mpox Response.

This Viewpoint discusses 3 key lessons from the mpox response that can help better manage infectious disease outbreaks in the future.

Cross-cultural adaptation of Nepalese literacy and stigma of suicide scales (LOSS-SF-Nep and SOSS-SF-Nep) among Nepalese medical and nursing students.

INTRODUCTION: Nepal is a country in South-east Asia with high suicide. There is ongoing trend of emerging research on suicide from Nepal but there is lack of validated scale in measuring literacy or stigma. In the view of poor media reporting and large treatment gap, this study was conducted. All previous validation studies were done in non-Hindu populations. METHODS: A cross-sectional study was planned where the short forms of Nepalese literacy of suicide scale (LOSS-SF-Nep) and Stigma of Suicide Scale (SOSS-SF-Nep) were validated using standard procedure at a medical college in southern Nepal. Medical and nursing students of all batches were approached offline after successful pretesting. The psychometric properties of the scales were tested, and the statuses of literacy and stigma were assessed. Patient Health Questionnaire-9 and General Anxiety Disorder Scale-7 were used for revealing depression and generalized anxiety. RESULTS: Three hundred and nineteen Nepalese students participated and most of them were males, belonged to nuclear family, upper-middle socioeconomic status and represented 46 out of 77 districts of Nepal. The mean score of LOSS-SF-Nep was 6.36 ± 1.92 and literacy ranged from 37.9% to 89.7%. The deeper exposure to suicidal patients was associated with better literacy. Factor analysis of SOSS-SF-Nep revealed three subscales: stigmatization, isolation/depression, and normalization/glorification and had acceptable psychometric properties. Gender, occupation of head of the family, region and years of education, using mental health services, and depression were associated with variable literacy or stigma. CONCLUSION: Literacy and stigma scales were validated in Nepali, and SOSS factor structures were revealed with modified descriptors. The literacy and stigma levels in medical students were calculated for the first time in Nepal and Hindu majority population.

Trends in performance of hospital outpatient procedures and associated 30-day costs among Medicare beneficiaries from 2011 to 2018.

BACKGROUND: United States healthcare has increasingly transitioned to outpatient care delivery. The degree to which Academic Medical Centers (AMCs) have been able to shift surgical procedures from inpatient to outpatient settings despite higher patient complexity is unknown. METHODS: This observational study used a 20% sample of fee-for-service Medicare beneficiaries age 65 and older undergoing eight elective procedures from 2011 to 2018 to model trends in procedure site (hospital outpatient vs. inpatient) and 30-day standardized Medicare costs, overall and by hospital teaching status. RESULTS: Of the 1,222,845 procedures, 15.9% occurred at AMCs. There was a 2.42% per-year adjusted increase (95% CI 2.39%-2.45%; p < .001) in proportion of outpatient hospital procedures, from 68.9% in 2011 to 85.4% in 2018. Adjusted 30-day standardized costs declined from $18,122 to $14,353, (-$560/year, 95% CI -$573 to -$547; p < .001). Patients at AMCs had more chronic conditions and higher predicted annual mortality. AMCs had a lower proportion of outpatient procedures in all years compared to non-AMCs, a difference that was statistically significant but small in magnitude. AMCs had higher costs compared to non-AMCs and a lesser decline over time (p < .001 for the interaction). AMCs and non-AMCs saw a similar decline in 30-day mortality. CONCLUSIONS: There has been a substantial shift toward outpatient procedures among Medicare beneficiaries with a decrease in total 30-day Medicare spending as well as 30-day mortality. Despite a higher complexity population, AMCs shifted procedures to the outpatient hospital setting at a similar rate as non-AMCs. IMPLICATIONS: The trend toward outpatient procedural care and lower spending has been observed broadly across AMCs and non-AMCs, suggesting that Medicare beneficiaries have benefited from more efficient delivery of procedural care across academic and community hospitals.

External Validation of Robust Radiomic Signature to Predict 2-Year Overall Survival in Non-Small-Cell Lung Cancer.

Lung cancer is the second most fatal disease worldwide. In the last few years, radiomics is being explored to develop prediction models for various clinical endpoints in lung cancer. However, the robustness of radiomic features is under question and has been identified as one of the roadblocks in the implementation of a radiomic-based prediction model in the clinic. Many past studies have suggested identifying the robust radiomic feature to develop a prediction model. In our earlier study, we identified robust radiomic features for prediction model development. The objective of this study was to develop and validate the robust radiomic signatures for predicting 2-year overall survival in non-small cell lung cancer (NSCLC). This retrospective study included a cohort of 300 stage I-IV NSCLC patients. Institutional 200 patients' data were included for training and internal validation and 100 patients' data from The Cancer Image Archive (TCIA) open-source image repository for external validation. Radiomic features were extracted from the CT images of both cohorts. The feature selection was performed using hierarchical clustering, a Chi-squared test, and recursive feature elimination (RFE). In total, six prediction models were developed using random forest (RF-Model-O, RF-Model-B), gradient boosting (GB-Model-O, GB-Model-B), and support vector(SV-Model-O, SV-Model-B) classifiers to predict 2-year overall survival (OS) on original data as well as balanced data. Model validation was performed using 10-fold cross-validation, internal validation, and external validation. Using a multistep feature selection method, the overall top 10 features were chosen. On internal validation, the two random forest models (RF-Model-O, RF-Model-B) displayed the highest accuracy; their scores on the original and balanced datasets were 0.81 and 0.77 respectively. During external validation, both the random forest models' accuracy was 0.68. In our study, robust radiomic features showed promising predictive performance to predict 2-year overall survival in NSCLC.

Emerging role of quantitative imaging (radiomics) and artificial intelligence in precision oncology.

Cancer is a fatal disease and the second most cause of death worldwide. Treatment of cancer is a complex process and requires a multi-modality-based approach. Cancer detection and treatment starts with screening/diagnosis and continues till the patient is alive. Screening/diagnosis of the disease is the beginning of cancer management and continued with the staging of the disease, planning and delivery of treatment, treatment monitoring, and ongoing monitoring and follow-up. Imaging plays an important role in all stages of cancer management. Conventional oncology practice considers that all patients are similar in a disease type, whereas biomarkers subgroup the patients in a disease type which leads to the development of precision oncology. The utilization of the radiomic process has facilitated the advancement of diverse imaging biomarkers that find application in precision oncology. The role of imaging biomarkers and artificial intelligence (AI) in oncology has been investigated by many researchers in the past. The existing literature is suggestive of the increasing role of imaging biomarkers and AI in oncology. However, the stability of radiomic features has also been questioned. The radiomic community has recognized that the instability of radiomic features poses a danger to the global generalization of radiomic-based prediction models. In order to establish radiomic-based imaging biomarkers in oncology, the robustness of radiomic features needs to be established on a priority basis. This is because radiomic models developed in one institution frequently perform poorly in other institutions, most likely due to radiomic feature instability. To generalize radiomic-based prediction models in oncology, a number of initiatives, including Quantitative Imaging Network (QIN), Quantitative Imaging Biomarkers Alliance (QIBA), and Image Biomarker Standardisation Initiative (IBSI), have been launched to stabilize the radiomic features.

Machine-Learning-Based Radiomics for Classifying Glioma Grade from Magnetic Resonance Images of the Brain.

Grading of gliomas is a piece of critical information related to prognosis and survival. Classifying glioma grade by semantic radiological features is subjective, requires multiple MRI sequences, is quite complex and clinically demanding, and can very often result in erroneous radiological diagnosis. We used a radiomics approach with machine learning classifiers to determine the grade of gliomas. Eighty-three patients with histopathologically proven gliomas underwent MRI of the brain. Whenever available, immunohistochemistry was additionally used to augment the histopathological diagnosis. Segmentation was performed manually on the T2W MR sequence using the TexRad texture analysis softwareTM, Version 3.10. Forty-two radiomics features, which included first-order features and shape features, were derived and compared between high-grade and low-grade gliomas. Features were selected by recursive feature elimination using a random forest algorithm method. The classification performance of the models was measured using accuracy, precision, recall, f1 score, and area under the curve (AUC) of the receiver operating characteristic curve. A 10-fold cross-validation was adopted to separate the training and the test data. The selected features were used to build five classifier models: support vector machine, random forest, gradient boost, naive Bayes, and AdaBoost classifiers. The random forest model performed the best, achieving an AUC of 0.81, an accuracy of 0.83, f1 score of 0.88, a recall of 0.93, and a precision of 0.85 for the test cohort. The results suggest that machine-learning-based radiomics features extracted from multiparametric MRI images can provide a non-invasive method for predicting glioma grades preoperatively. In the present study, we extracted the radiomics features from a single cross-sectional image of the T2W MRI sequence and utilized these features to build a fairly robust model to classify low-grade gliomas from high-grade gliomas (grade 4 gliomas).

Systematic review and meta-analysis of prediction models used in cervical cancer.

BACKGROUND: Cervical cancer is one of the most common cancers in women with an incidence of around 6.5 % of all the cancer in women worldwide. Early detection and adequate treatment according to staging improve the patient's life expectancy. Outcome prediction models might aid treatment decisions, but a systematic review on prediction models for cervical cancer patients is not available. DESIGN: We performed a systematic review for prediction models in cervical cancer following PRISMA guidelines. Key features that were used for model training and validation, the endpoints were extracted from the article and data were analyzed. Selected articles were grouped based on prediction endpoints i.e. Group1: Overall survival, Group2: progression-free survival; Group3: recurrence or distant metastasis; Group4: treatment response; Group5: toxicity or quality of life. We developed a scoring system to evaluate the manuscript. As per our criteria, studies were divided into four groups based on scores obtained in our scoring system, the Most significant study (Score > 60 %); Significant study (60 % > Score > 50 %); Moderately Significant study (50 % > Score > 40 %); least significant study (score < 40 %). A meta-analysis was performed for all the groups separately. RESULTS: The first line of search selected 1358 articles and finally 39 articles were selected as eligible for inclusion in the review. As per our assessment criteria, 16, 13 and 10 studies were found to be the most significant, significant and moderately significant respectively. The intra-group pooled correlation coefficient for Group1, Group2, Group3, Group4, and Group5 were 0.76 [0.72, 0.79], 0.80 [0.73, 0.86], 0.87 [0.83, 0.90], 0.85 [0.77, 0.90], 0.88 [0.85, 0.90] respectively. All the models were found to be good (prediction accuracy [c-index/AUC/R2] >0.7) in endpoint prediction. CONCLUSIONS: Prediction models of cervical cancer toxicity, local or distant recurrence and survival prediction show promising results with reasonable prediction accuracy [c-index/AUC/R2 > 0.7]. These models should also be validated on external data and evaluated in prospective clinical studies.

A dosimetric comparison of systemic peptide receptor radionuclide therapy and intra-arterial peptide receptor radionuclide therapy in patients with liver dominant gastroenteropancreatic neuroendocrine tumours.

OBJECTIVES: Intra-arterial radionuclide therapy (IART) treatment allows direct delivery of 177 Lu-DOTATATE to the overexpressed somatostatin-positive neuroendocrine liver metastases, which led to higher tumour concentration compared with systemic radionuclide therapy (SRT). The aim was to evaluate and compare the absorbed doses of both IART and SRT to organs and hepatic metastatic sites. METHODS: A total of 48 patients received SRT and IART. In SRT, activity was administered intravenously, whereas in IART, activity was administered directly into hepatic arteries. The sequential whole-body images were acquired at 2, 4, 24, 72 and 160 h. The reconstructed whole-body planar and single-photon emission computed tomography-computed tomography images were processed using the Dosimetry Toolkit for the estimation of normalized cumulated activity in the organs and tumour lesions. The absorbed dose was computed using OLINDA EXM 2.0 software. RESULTS: The median absorbed dose (mGy/MBq) of kidneys and spleen in IART was compared with SRT and found to be decreased by 30.7% ( P  = 0.03) and 37.5% ( P  = 0.08), whereas it was found to be increased by 40% ( P  = 0.26) and 8.1% ( P  = 0.28) in the liver and lungs. The median dose (mGy/MBq) of tumours determined in IART was found to be increased by 62.2% ( P  = 0.04). CONCLUSION: IART with 177 Lu-DOTATATE significantly increases tumour dose while reducing overall systemic toxicity in comparison to SRT treatment. After considering the maximum tolerance limit of kidneys in peptide receptor radionuclide therapy, the number of treatment cycles and injected activity can be optimized further with IART for better response and survival.

Large Anorectal Venous Malformation Treated With Microfoam Therapy.

Deficiency of smooth muscle cells can lead to dysfunction and engorgement of blood vessels termed as hemangioma, arteriovenous malformations, and venous malformations (VMs). Anorectal VM is a rare disease. It can present with massive hematochezia. An optimal treatment of anorectal VMs has not been defined. Surgery is an option if the lesion can be resected completely. Endoscopic injection sclerotherapy has been reported to be effective in treating small colorectal VMs. However, it has rarely been described in the treatment of large VMs. In this study, we describe a rare case of large anorectal VMs treated with microfoam sclerotherapy.

Cyber Attacker Profiling for Risk Analysis Based on Machine Learning.

The notion of the attacker profile is often used in risk analysis tasks such as cyber attack forecasting, security incident investigations and security decision support. The attacker profile is a set of attributes characterising an attacker and their behaviour. This paper analyzes the research in the area of attacker modelling and presents the analysis results as a classification of attacker models, attributes and risk analysis techniques that are used to construct the attacker models. The authors introduce a formal two-level attacker model that consists of high-level attributes calculated using low-level attributes that are in turn calculated on the basis of the raw security data. To specify the low-level attributes, the authors performed a series of experiments with datasets of attacks. Firstly, the requirements of the datasets for the experiments were specified in order to select the appropriate datasets, and, afterwards, the applicability of the attributes formed on the basis of such nominal parameters as bash commands and event logs to calculate high-level attributes was evaluated. The results allow us to conclude that attack team profiles can be differentiated using nominal parameters such as bash history logs. At the same time, accurate attacker profiling requires the extension of the low-level attributes list.

Serum Gamma-Glutamyl Transpeptidase-to-Platelet Ratio as a Noninvasive Marker of Liver Fibrosis in Chronic Hepatitis B.

Background The gamma-glutamyl transpeptidase (GGT)-to-platelet ratio (GPR) is identified as a new model for the assessment of liver fibrosis in patients with chronic hepatitis B (CHB). We aimed to determine the diagnostic performance of GPR for the prediction of liver fibrosis in patients with CHB. Methods In an observational cohort study, patients with CHB were enrolled. The diagnostic performance of GPR was compared with transient elastography (TE), aspartate aminotransferase-to-platelet ratio index (APRI), and fibrosis-4 (FIB-4) scores for the prediction of liver fibrosis using liver histology as a gold standard. Results Forty-eight patients with CHB with a mean age of 33.42 ± 15.72 years were enrolled. Liver histology showed meta-analysis of histological data in viral hepatitis (METAVIR) stage F0, F1, F2, F3, and F4 fibrosis in 11, 12, 11, seven, and seven patients, respectively. The Spearman correlation of METAVIR fibrosis stage with APRI, FIB-4, GPR, and TE were 0.354, 0.402, 0.551, and 0.726, respectively (P value < 0.05). TE had the highest sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) (80%, 83%, 83%, and 79%, respectively), followed by GPR (76%, 65%, 70%, and 71%, respectively) for predicting significant fibrosis (≥F2). However, TE had comparable sensitivity, specificity, PPV, and NPV with GPR (86%, 82%, 42%, and 93%, and 86%, 71%, 42%, and 92%, respectively) for predicting extensive fibrosis (≥F3). Conclusion The performance of GPR is comparable to TE in predicting significant and extensive liver fibrosis. GPR may be an acceptable, low-cost alternative for predicting compensated advanced chronic liver disease (cACLD) (F3-F4) in CHB patients.