Effect of treatment of chronic hepatitis c virus patients with direct-acting anti-retroviral drugs on semen and hormonal parameters.

Objective: Hepatitis C virus (HCV) infection is known to influence the seminal and hormonal parameters of infected men. This study was performed to assess the effects of HCV clearance using direct-acting antiviral (DAA) agents on semen and hormonal parameters. Methods: A total of 50 patients with chronic HCV were enrolled, and conventional semen analysis was performed according to World Health Organization guidelines. Basal levels of total testosterone, free testosterone (FT), follicle-stimulating hormone (FSH), luteinizing hormone (LH), estradiol (E2), prolactin, and sex hormone-binding globulin (SHBG) were assessed before and 3 months after treatment with DAAs. Results: Following DAA treatment, statistically significant increases were observed in sperm motility and the proportion of grade A sperm. Additionally, the percentage of abnormal forms was significantly decreased after treatment (p=0.000). However, no significant differences were observed in semen volume, concentration, or total sperm count. Sex hormone analysis of patients after DAA treatment revealed significant increases in FT, LH, and FSH levels, along with significant decreases in SHBG, prolactin, and E2 levels. Conclusion: Following HCV clearance, we noted an improvement in sperm motility and an increase in the percentage of sperm with normal morphology. Treatment with DAAs was also associated with increased levels of FT and LH, along with decreased levels of SHBG, prolactin, and E2.

Efficacy of Intralesional Candida Antigen Versus Measles, Mumps, and Rubella Vaccine Versus Topical Podophyllin in Treatment of Resistant Genital Warts.

BACKGROUND: No single treatment is ideal for genital warts with high rate of resistance using conventional modalities as topical podophyllin; however, several intralesional immunotherapies are being tested nowadays, with variable results. In this study, we compared the safety and efficacy of treating resistant and recurrent genital warts by 2 intralesional immunotherapies [Candida antigen and measles, mumps, and rubella (MMR) vaccine] and compared them with topical podophyllin. PATIENTS/METHODS: A total of 45 patients with resistant or recurrent genital warts were enrolled in this study. Size and number of warts were detected in each patient, patients were divided into 3 groups. Group A injected with intralesional Candida antigen. Group B with intralesional MMR vaccine. Group C were treated with topical 25% podophyllin. Patients received a session every 2 weeks for 3 treatment sessions. RESULTS: With regard to the reduction in size and number of all warts, the best response was obtained in Candida antigen group where 46.7% showed complete clearance and 40% showed partial response followed by MMR group and the last was the podophyllin group, with no significant difference between them. Complete clearance of mother warts was noticed in 86.7% of Candida group, 53.3% in MMR group, and last 40% in podophyllin group, with a significantly better response in the Candida group (P = .027). CONCLUSION: Both intralesional Candida antigen and MMR vaccine are simple, safe, and effective treatment options with comparable results and better response than topical podophyllin.

Foundation model for cancer imaging biomarkers.

Foundation models in deep learning are characterized by a single large-scale model trained on vast amounts of data serving as the foundation for various downstream tasks. Foundation models are generally trained using self-supervised learning and excel in reducing the demand for training samples in downstream applications. This is especially important in medicine, where large labelled datasets are often scarce. Here, we developed a foundation model for cancer imaging biomarker discovery by training a convolutional encoder through self-supervised learning using a comprehensive dataset of 11,467 radiographic lesions. The foundation model was evaluated in distinct and clinically relevant applications of cancer imaging-based biomarkers. We found that it facilitated better and more efficient learning of imaging biomarkers and yielded task-specific models that significantly outperformed conventional supervised and other state-of-the-art pretrained implementations on downstream tasks, especially when training dataset sizes were very limited. Furthermore, the foundation model was more stable to input variations and showed strong associations with underlying biology. Our results demonstrate the tremendous potential of foundation models in discovering new imaging biomarkers that may extend to other clinical use cases and can accelerate the widespread translation of imaging biomarkers into clinical settings.

Foundation Models for Quantitative Biomarker Discovery in Cancer Imaging.

Foundation models represent a recent paradigm shift in deep learning, where a single large-scale model trained on vast amounts of data can serve as the foundation for various downstream tasks. Foundation models are generally trained using self-supervised learning and excel in reducing the demand for training samples in downstream applications. This is especially important in medicine, where large labeled datasets are often scarce. Here, we developed a foundation model for imaging biomarker discovery by training a convolutional encoder through self-supervised learning using a comprehensive dataset of 11,467 radiographic lesions. The foundation model was evaluated in distinct and clinically relevant applications of imaging-based biomarkers. We found that they facilitated better and more efficient learning of imaging biomarkers and yielded task-specific models that significantly outperformed their conventional supervised counterparts on downstream tasks. The performance gain was most prominent when training dataset sizes were very limited. Furthermore, foundation models were more stable to input and inter-reader variations and showed stronger associations with underlying biology. Our results demonstrate the tremendous potential of foundation models in discovering novel imaging biomarkers that may extend to other clinical use cases and can accelerate the widespread translation of imaging biomarkers into clinical settings.

Characterization of Brucella abortus Mutant A19mut2, a Potential DIVA Vaccine Candidate with a Modification on Lipopolysaccharide.

BACKGROUND: Brucella abortus is the main causative agent for bovine brucellosis. B. abortus A19 is a widely used vaccine strain to protect cows from Brucella infection in China. However, A19 has a similar lipopolysaccharide (LPS) antigen to that of the field virulent Brucella strain, whose immunization interferes with the serodiagnosis of vaccinated and infected animals. [Aim] To develop a novel Brucella DIVA vaccine candidate. STUDY DESIGN AND METHODS: The B. abortus mutant A19mut2 with the formyltransferase gene wbkC is replaced by an acetyltransferase gene wbdR from E. coli O157 using the bacterial homologous recombination technique, generating a modified O-polysaccharide that cannot induce antibodies in mice against wild-type Brucella LPS. The biological phenotypes of the A19mut2 were assessed using a growth curve analysis, agglutination tests, Western blotting, and stress resistance assays. Histopathological changes and bacterial colonization in the spleens of vaccinated mice were investigated to assess the residual virulence and protection of the A19mut2. Humoral and cellular immunity was evaluated by measuring the levels of IgG, IgG subtypes, and the release of cytokines IFN-γ and IL10 in the splenocytes of the vaccinated mice. ELISA coated with wild-type LPS can distinguish mouse antibodies induced by A19 and A19mut2 immunization. RESULTS: The A19mut2 showed a decreased residual virulence in mice, compared to the A19 strain, but induced significant humoral and cellular immune responses, as the A19 immunization did. The protection efficacy of A19mut2 immunization against B. abortus S2308 NalR infection was similar to that of A19 immunization. CONCLUSION: The A19mut2 has potential as a novel DIVA vaccine candidate in the future.

Antioxidant, antiapoptotic, and antifibrotic abilities of L-Arginine ameliorate the testicular dysfunction in diabetic rats.

Testicular dysfunction and infertility are serious complications of diabetes mellitus (DM). L-Arginine (L-Arg) is a semi essential amino acid with various biological and metabolic functions. The molecular mechanisms of L-Arg on testicular dysfunction caused by DM remain elusive. This study aimed to assess the potential protective effect of L-Arg in diabetic testis and its possible mechanisms. 24 adult male Wistar albino rats were randomly divided into four groups: CON, L-Arg that received 1 g/kg body weight of L-Arg orally for 4 weeks, DM that fed a high fat diet followed by an injection of 30 mg/kg streptozotocin intraperitoneally, and L-Arg-treated DM that were diabetic and administered L-Arg. DM decreased relative testicular weight, reduced serum testosterone, and impaired semen parameters. Reduced total antioxidant capacity (TAC), superoxide dismutase (SOD), and glutathione peroxidase (GSH-Px), in addition to increased transforming growth factor B1 (TGF-ß1) and nitric oxide (NO) levels, were found in the testicular tissue. This was associated with severe degenerative changes in the seminiferous tubules and interstitial cells of Leydig, reduction of Johnsen's score, significantly increased expression of both inducible nitric oxide synthase (iNOS) and caspase-3, and reduced zonula occludens (ZO)- 1 expression. Ultrastructurally, disrupted intercellular junctions and degeneration of interstitial cells of Leydig were observed. In contrast, treatment of diabetic animals with L-Arg increased TAC, SOD and GSH-Px, decreased TGF-ß1 and NO levels, downregulated iNOS and caspase-3 expression, upregulated ZO-1 expression, and maintained the integrity of the Sertoli cell junctions. Hence, L-Arg restored the normal testicular structure and function via its antioxidant, antiapoptotic, and antifibrotic effects.

Novel protective effect of diosmin against cisplatin-induced prostate and seminal vesicle damage: Role of oxidative stress and apoptosis.

Multiple organ toxicity has been associated with cisplatin (CIS) treatment, limiting its clinical use. The human prostate and seminal vesicles are accessory sex organs with androgen-dependent morphogenesis, growth, and secretion. The present study aimed to investigate, for the first time, the toxic effect of CIS on normal prostate and seminal vesicles in the presence and absence of diosmin (DS). The animals were randomized into 4 groups as follows: control (received vehicle), CIS group (7.5 mg/kg, i.p. on 5th and 12th day), DS group (100 mg/kg, p.o. for 15 days), and DS+CIS group. Histopathological and biochemical analyses were conducted to elucidate the goal of this study. CIS administration significantly induced prostate and seminal vesicle toxicity as evidenced by alteration of serum testosterone, LH, FSH, PSA, steroidogenic HSD17B6 as well as seminal analysis markers. Remarkably, marked histopathological changes in thin and ultrathin structures were observed. Besides, CIS significantly boosted oxidative stress as evidenced by the up-regulation of MDA and down-regulation of TAC. CIS significantly induced tissue apoptosis concomitant with suppression of cellular proliferation and stem cell expression as indicated by up-regulation of activated caspase-3 and Bax expression along with down-regulation of Bcl-2, Ki67, and CD44 expression. Interestingly, DS fixed all disturbances in the prostate and seminal vesicles induced by CIS. Together, CIS could cause prostate and seminal vesicle toxicity by affecting hormonal, steroidogenic, oxidative stress, apoptosis, and proliferation processes, and this effect was reversed by DS administration.

Clinical validation of deep learning algorithms for radiotherapy targeting of non-small-cell lung cancer: an observational study.

BACKGROUND: Artificial intelligence (AI) and deep learning have shown great potential in streamlining clinical tasks. However, most studies remain confined to in silico validation in small internal cohorts, without external validation or data on real-world clinical utility. We developed a strategy for the clinical validation of deep learning models for segmenting primary non-small-cell lung cancer (NSCLC) tumours and involved lymph nodes in CT images, which is a time-intensive step in radiation treatment planning, with large variability among experts. METHODS: In this observational study, CT images and segmentations were collected from eight internal and external sources from the USA, the Netherlands, Canada, and China, with patients from the Maastro and Harvard-RT1 datasets used for model discovery (segmented by a single expert). Validation consisted of interobserver and intraobserver benchmarking, primary validation, functional validation, and end-user testing on the following datasets: multi-delineation, Harvard-RT1, Harvard-RT2, RTOG-0617, NSCLC-radiogenomics, Lung-PET-CT-Dx, RIDER, and thorax phantom. Primary validation consisted of stepwise testing on increasingly external datasets using measures of overlap including volumetric dice (VD) and surface dice (SD). Functional validation explored dosimetric effect, model failure modes, test-retest stability, and accuracy. End-user testing with eight experts assessed automated segmentations in a simulated clinical setting. FINDINGS: We included 2208 patients imaged between 2001 and 2015, with 787 patients used for model discovery and 1421 for model validation, including 28 patients for end-user testing. Models showed an improvement over the interobserver benchmark (multi-delineation dataset; VD 0·91 [IQR 0·83-0·92], p=0·0062; SD 0·86 [0·71-0·91], p=0·0005), and were within the intraobserver benchmark. For primary validation, AI performance on internal Harvard-RT1 data (segmented by the same expert who segmented the discovery data) was VD 0·83 (IQR 0·76-0·88) and SD 0·79 (0·68-0·88), within the interobserver benchmark. Performance on internal Harvard-RT2 data segmented by other experts was VD 0·70 (0·56-0·80) and SD 0·50 (0·34-0·71). Performance on RTOG-0617 clinical trial data was VD 0·71 (0·60-0·81) and SD 0·47 (0·35-0·59), with similar results on diagnostic radiology datasets NSCLC-radiogenomics and Lung-PET-CT-Dx. Despite these geometric overlap results, models yielded target volumes with equivalent radiation dose coverage to those of experts. We also found non-significant differences between de novo expert and AI-assisted segmentations. AI assistance led to a 65% reduction in segmentation time (5·4 min; p<0·0001) and a 32% reduction in interobserver variability (SD; p=0·013). INTERPRETATION: We present a clinical validation strategy for AI models. We found that in silico geometric segmentation metrics might not correlate with clinical utility of the models. Experts' segmentation style and preference might affect model performance. FUNDING: US National Institutes of Health and EU European Research Council.

Isolation, Molecular Characterization, and Antibiotic Resistance of Avian Pathogenic Escherichia coli in Eastern China.

Avian pathogenic Escherichia coli (APEC) causes colibacillosis in avians, resulting in considerable losses in the poultry industry. APEC showed zoonotic potential initially related to the fact that APEC serves as the reservoir of virulence genes and antibiotic resistance genes for other E. coli. Thus, we determine the serotypes, phylogenetic groups, virulence genes distribution, and antibiotic resistance profiles of APEC isolates in eastern China. A total of 230 APEC were isolated from diseased chicken and duck with typical colibacillosis symptoms. Serotyping identified that O78 (44.78%) was the predominant serotype. The majority of APEC isolates were classified into B2 (29.57%), A (26.96%), D (20.00%), and B1 (18.26%), respectively. Among the 15 virulence genes, a high prevalence of ibeB (99.57%), fimC (91.74%), mat (91.30%), ompA (83.04%), and iss (80.43%) genes was observed. Except for low resistance rates for imipenem (1.7%) and polymyxin B (0.4%), most of the APEC isolates were resistant to erythromycin (98.7%), enrofloxacin (96.1%), tetracycline (95.2%), doxycycline (93.9%), lincomycin (90.0%), and streptomycin (90.0%). Moreover, all APEC exhibit multi-drug resistance. This study indicated that APEC isolates harbor a variety of virulence genes and showed multi-antibiotic resistance profiles, providing proof for understanding the epidemiological background and zoonotic potential of APEC in poultry farms.

Deep Learning-based Detection of Intravenous Contrast Enhancement on CT Scans.

Identifying the presence of intravenous contrast material on CT scans is an important component of data curation for medical imaging-based artificial intelligence model development and deployment. Use of intravenous contrast material is often poorly documented in imaging metadata, necessitating impractical manual annotation by clinician experts. Authors developed a convolutional neural network (CNN)-based deep learning platform to identify intravenous contrast enhancement on CT scans. For model development and validation, authors used six independent datasets of head and neck (HN) and chest CT scans, totaling 133 480 axial two-dimensional sections from 1979 scans, which were manually annotated by clinical experts. Five CNN models were trained first on HN scans for contrast enhancement detection. Model performances were evaluated at the patient level on a holdout set and external test set. Models were then fine-tuned on chest CT data and externally validated. This study found that Digital Imaging and Communications in Medicine metadata tags for intravenous contrast material were missing or erroneous for 1496 scans (75.6%). An EfficientNetB4-based model showed the best performance, with areas under the curve (AUCs) of 0.996 and 1.0 in HN holdout (n = 216) and external (n = 595) sets, respectively, and AUCs of 1.0 and 0.980 in the chest holdout (n = 53) and external (n = 402) sets, respectively. This automated, scan-to-prediction platform is highly accurate at CT contrast enhancement detection and may be helpful for artificial intelligence model development and clinical application. Keywords: CT, Head and Neck, Supervised Learning, Transfer Learning, Convolutional Neural Network (CNN), Machine Learning Algorithms, Contrast Material Supplemental material is available for this article. © RSNA, 2022.

Contribution of interleukin 27 serum level to pathogenesis and prognosis in children with immune thrombocytopenia.

ABSTRACT: Concepts surrounding the mechanisms of thrombocytopenia in ITP have shifted from the traditional view of autoantibody mediated platelet destruction to more complex mechanisms in which impaired platelet production, T-cell-mediated effects, and disturbed cytokine profiles play a role. Interleukin 27 (IL-27) plays pleiotropic roles in immunomodulation and autoimmune diseases.We aimed to determine the level of IL-27 in patients with ITP and its relationship to patient and disease characteristics as well as disease chronicity and response to treatment.Sixty childrens with primary immune thrombocytopenia were consequetively enrolled in this study as well as 20 age and sex matched healthy controls.ITP patients had significantly higher levels of IL-27 than controls (770.6 and 373.8 pg/ml, respectively). Patients with acute ITP had the highest levels of IL-27 among patient groups, while patients in remission had the lowest IL-27 levels (860.1and 622.9 pg/ml, respectively). Patients who received IVIG and combined steroids plus IVIG had significantly higher IL-27 levels than others. Patients who received Eltrombopag had significantly lower IL-27 levels than others.IL-27 seems to play a role in pathogenesis of childhood ITP. IL-27 can be used as a predictor for disease occurrence as well as responsiveness to treatment.

Effect of Light Conditions and Accommodation on Implantable Phakic Contact Lens Vault by Anterior Segment Optical Coherence Tomography.

Purpose: To investigate the dynamic pupil and vault changes in eyes with implantable phakic contact lens (IPCL) under photopic and scotopic settings, as well as during accommodation using the anterior segment optical coherence tomography (AS-OCT). Methods: A prospective observational study included consecutive 36 eyes of myopic patients who underwent IPCL V2.0 implantation. Under photopic and scotopic light settings, as well as during accommodation, all patients were scanned using CASIA OCT (CASIA2; TOMEY, Nagoya, Japan). The pupil size, the vault (distance between the back surface of the IPCL and the anterior lens capsule), ACD-lens (distance between the posterior corneal surface and the anterior lens surface), IPCL-lens (distance between the posterior corneal surface and the anterior IPCL surface), and lens thickness (LT) were the study parameters. Results: The vault was significantly lower under photopic conditions (p-value<0.001). The pupil size was significantly smaller in photopic conditions (p-value<0.001). LT (p-value=0.975) and ACD-lens (p-value=0.917) were not significantly different between scotopic and photopic conditions, while the ACD-IPCL was significantly larger during photopic conditions (p-value=0.013). There were significant changes in all parameters between accommodative and non-accommodative conditions. Conclusion: The IPCL vault decreased significantly under photopic light conditions and accommodation.

Evaluation of the anterior chamber angle by anterior segment optical coherence tomography after implantable phakic contact lens implantation in myopic eyes.

PURPOSE: To evaluate the changes in the angle of the AC and lens vault after IPCL implantation by AS-OCT in myopic patients. METHODS: This was a prospective observational study involving 30 myopic eyes implanted with IPCL. AS-OCT was used to evaluate lens vault and AC angle parameters including anterior chamber angle, angle opening distance and trabecular-iris space area (TISA) at 1, 3 and 6 months postoperatively. RESULTS: All 3 AC angle parameters were significantly reduced at the 1st postoperative month compared to preoperative values, but remained stable thereafter with no significant change at the 3rd or 6th postoperative months. The lens vault showed no significant change over the entire follow-up period. CONCLUSION: IPCL implantation is a safe method for correction of myopia with stable AC angle narrowing over the course of 6 months postoperatively as monitored using AS-OCT.

The novel LysR-family transcriptional regulator BvtR is involved in the resistance of Brucella abortus to nitrosative stress, detergents and virulence through the genetic regulation of diverse pathways.

Brucella is a facultative intracellular bacterium lacking classical virulence factors; its virulence instead depends on its ability to invade and proliferate within host cells. After entering cells, Brucella rapidly modulates the expression of a series of genes involved in metabolism and immune evasion. Here, a novel LysR-family transcriptional regulator, designated Brucellavirulence-related transcriptional regulator (BvtR), was found to be associated with Brucella abortus virulence. We first successfully constructed a BvtR mutant, ΔbvtR, and a complemented strain, ΔbvtR-Com. Subsequently, we performed cell infection experiments, which indicated that the ΔbvtR strain exhibited similar adhesion, invasion and survival within HeLa cells or RAW264.7 macrophages to those of the wild-type strain. In stress resistance tests, the ΔbvtR strain showed enhanced sensitivity to sodium nitroprusside and sodium dodecyl sulfate, but not to hydrogen peroxide, cumene hydroperoxide, polymyxin B and natural serum. Mouse infection experiments indicated that the virulence of the ΔbvtR strain significantly decreased at 4 weeks post-infection. Finally, we analyzed differentially expressed genes regulated by BvtR with RNA-seq, COG classification and KEGG pathway analysis. Nitrogen metabolism, siderophore biosynthesis and oligopeptide transport were found to be the predominantly altered functions, and key metabolic and regulatory networks were delineated in the ΔbvtR mutant. Thus, we identified a novel Brucella virulence-related regulator, BvtR, and demonstrated that BvtR regulation affects Brucella resistance to killing by sodium nitroprusside and sodium dodecyl sulfate. The differentially expressed genes responding to BvtR are involved in diverse functions or pathways in Brucella, thus, suggesting the breadth of BvtR's regulatory functions. This study provides novel clues regarding Brucella pathogenesis.

Perceptions and Attitudes of Hospital' Prescribers towards Drug Information Sources and Prescribing Practices

Abstract Healthcare professionals use a variety of drug information sources to fulfill their clinical needs and medical practice. The aim of present study was to assess the sources of drug information among hospital' prescribers and evaluate their prescribing behavior in Saudi hospitals. A cross-sectional survey was conducted among randomly selected hospital' prescribers using a self-administered questionnaire. The response rate to the survey was 64.29%, with a ratio of 76.44% male and 23.56% female. The internet 137(60.89%) and textbooks 86(38.22%) were the prevalent sources for drug information used. Up-To-Date 107(47.56%), Medscape 105(46.67%) and FDA 74(32.88%) were the common electronic drug sources used. About 151(67.11%) of hospital' prescribers considered the pharmacist as a reliable drug information source. The most favored drug requests by hospital' prescribers from the pharmacists were drug alternatives 110(48.89%) followed by drug interactions 94(41.78%), side effects 78(34.67%) and indications 60(26.67%). Therapeutic efficacy 168(74.67%) and drug availability 73(32.44%) were the main factors contributed to the selection of drugs. This study shows some differences in hospital prescribers' perceptions of sources of drug information depending upon their background and clinical practice. Therefore, knowing appropriate drug information used by hospital' prescribers is fundamental for drug efficacy and safety in clinical practice.

End-to-End Non-Small-Cell Lung Cancer Prognostication Using Deep Learning Applied to Pretreatment Computed Tomography.

PURPOSE: Clinical TNM staging is a key prognostic factor for patients with lung cancer and is used to inform treatment and monitoring. Computed tomography (CT) plays a central role in defining the stage of disease. Deep learning applied to pretreatment CTs may offer additional, individualized prognostic information to facilitate more precise mortality risk prediction and stratification. METHODS: We developed a fully automated imaging-based prognostication technique (IPRO) using deep learning to predict 1-year, 2-year, and 5-year mortality from pretreatment CTs of patients with stage I-IV lung cancer. Using six publicly available data sets from The Cancer Imaging Archive, we performed a retrospective five-fold cross-validation using pretreatment CTs of 1,689 patients, of whom 1,110 were diagnosed with non-small-cell lung cancer and had available TNM staging information. We compared the association of IPRO and TNM staging with patients' survival status and assessed an Ensemble risk score that combines IPRO and TNM staging. Finally, we evaluated IPRO's ability to stratify patients within TNM stages using hazard ratios (HRs) and Kaplan-Meier curves. RESULTS: IPRO showed similar prognostic power (concordance index [C-index] 1-year: 0.72, 2-year: 0.70, 5-year: 0.68) compared with that of TNM staging (C-index 1-year: 0.71, 2-year: 0.71, 5-year: 0.70) in predicting 1-year, 2-year, and 5-year mortality. The Ensemble risk score yielded superior performance across all time points (C-index 1-year: 0.77, 2-year: 0.77, 5-year: 0.76). IPRO stratified patients within TNM stages, discriminating between highest- and lowest-risk quintiles in stages I (HR: 8.60), II (HR: 5.03), III (HR: 3.18), and IV (HR: 1.91). CONCLUSION: Deep learning applied to pretreatment CT combined with TNM staging enhances prognostication and risk stratification in patients with lung cancer.

Artificial intelligence for clinical oncology.

Clinical oncology is experiencing rapid growth in data that are collected to enhance cancer care. With recent advances in the field of artificial intelligence (AI), there is now a computational basis to integrate and synthesize this growing body of multi-dimensional data, deduce patterns, and predict outcomes to improve shared patient and clinician decision making. While there is high potential, significant challenges remain. In this perspective, we propose a pathway of clinical cancer care touchpoints for narrow-task AI applications and review a selection of applications. We describe the challenges faced in the clinical translation of AI and propose solutions. We also suggest paths forward in weaving AI into individualized patient care, with an emphasis on clinical validity, utility, and usability. By illuminating these issues in the context of current AI applications for clinical oncology, we hope to help advance meaningful investigations that will ultimately translate to real-world clinical use.

Improvement of sexual dysfunction in patients after treatment of hepatitis C virus using directly acting antivirals.

OBJECTIVE: The impact on male and female sexual dysfunction of treating hepatitis C virus (HCV) using direct-acting antiviral agents (DAAs) has not been sufficiently studied. The aim of this study was to assess the impact of HCV clearance with DAAs on sexual dysfunction (SD) in both sexes. METHODS: In chronic HCV patients who were eligible for DAAs, 100 sexually active men completed the Arabic version of the international index of erectile function questionnaire (IIEF-5), and the same number of sexually active women completed Female Sexual Function Index (FSFI), before, at the end of, and 3 months after, treatment for HCV. RESULT: The mean of the IIEF-5 scores for male patients was 16.29 ±.07 before treatment, 16.88 ± 3.63 3 months after treatment (p < .01), and was significantly higher, at 19.06 ± 3.31 6 months after treatment cessation (p < .01). In female patients, the mean total FSFI score at baseline was 19.22 ± 2.40 and after 3 months of treatment was 21.61 ± 3.45 (p < .01), with a significant increase (25.09 ± 4.52) after 6 months (p < .01). No difference in the improvement of sexual function was reported either after 3 months or at the end of treatment between males and females (p > .05). CONCLUSIONS: Significant improvement in SD associated with HCV infection in both sexes was recorded following viral clearance using DAAs treatment.

Deep learning classification of lung cancer histology using CT images.

Tumor histology is an important predictor of therapeutic response and outcomes in lung cancer. Tissue sampling for pathologist review is the most reliable method for histology classification, however, recent advances in deep learning for medical image analysis allude to the utility of radiologic data in further describing disease characteristics and for risk stratification. In this study, we propose a radiomics approach to predicting non-small cell lung cancer (NSCLC) tumor histology from non-invasive standard-of-care computed tomography (CT) data. We trained and validated convolutional neural networks (CNNs) on a dataset comprising 311 early-stage NSCLC patients receiving surgical treatment at Massachusetts General Hospital (MGH), with a focus on the two most common histological types: adenocarcinoma (ADC) and Squamous Cell Carcinoma (SCC). The CNNs were able to predict tumor histology with an AUC of 0.71(p = 0.018). We also found that using machine learning classifiers such as k-nearest neighbors (kNN) and support vector machine (SVM) on CNN-derived quantitative radiomics features yielded comparable discriminative performance, with AUC of up to 0.71 (p = 0.017). Our best performing CNN functioned as a robust probabilistic classifier in heterogeneous test sets, with qualitatively interpretable visual explanations to its predictions. Deep learning based radiomics can identify histological phenotypes in lung cancer. It has the potential to augment existing approaches and serve as a corrective aid for diagnosticians.

Characteristics of Brucella abortus vaccine strain A19 reveals its potential mechanism of attenuated virulence.

Brucella vaccination is one of the most important strategies for controlling brucellosis in livestock. The A19 strain was the effective vaccine used to control brucellosis in China. However, the characteristics of physiological and attenuated virulence of the A19 strain are not investigated in detail. In this study, we compared the phenotypic characteristics of the A19 to the wild-type strain S2308. Virulence test showed that the A19 was significantly attenuated at chronic infection stage in infected mouse model. In growth analysis, the A19 exhibited a quick growth at exponential phase and premature at stationary phase. The inflammatory response of macrophages infected by the A19 was detected using TaqMan qPCR assay, indicating that the inflammatory level of the A19-infected macrophages was higher than that of the S2308 infection. Cell death analysis showed that the A19 was not cytotoxic for macrophages. Cell infection showed that the A19 reduced its ability to invade, survive and traffic within host cells, and the intracellular A19 hardly excludes lysosome-associated marker LAMP-1, suggesting that the A19 can't escape the lysosome degradation within host cells. In further study, the sensitivity test exhibited that the A19 is more sensitive to stress and bactericidal factors than the S2308 strain, Western blot and silver staining analysis exhibited that the A19 has a different expression pattern of OMPs and reduces LPS O-antigen expression relative to the S2308 strain. Those data give us a more detailed understanding about the A19 vaccine strain, which will be beneficial for improvement of current Brucella vaccine and overcoming its defects.