Malignant pleural mesothelioma: The disdained member of thoracic oncology!

Pleural mesothelioma is a very aggressive malignancy that arises from the pleural mesothelial cell lining and is linked strongly to prior asbestos exposure. The ban on asbestos has helped to lower the incidence, but in developing countries like India, it is expected to rise. It has an extended latency period usually progressing over decades and presents with nonspecific symptoms. It has a median survival ranging between 10-22 months. The diagnosis of malignant pleural mesothelioma is challenging and is done using computed tomography (CT), magnetic resonance imaging, or positron emission tomography-CT, with the last two predicting the resectability of the tumor better than CT alone. A pleural biopsy along with an array of immunohistochemical markers, such as p16, BRCA1 associated protein 1, and claudin-4, are required for a definitive diagnosis. Several genetic alterations have prognostic significance as well. The current histological subtype identification is indispensable for decision making because of the new therapeutic avenues being explored. The combination of nivolumab and ipilimumab-based immunotherapy outperformed platinum and pemetrexed-based chemotherapy in terms of survival benefit and improved quality of life especially for non-epithelioid subtypes. However, the latter continues to be a robust treatment option for patients with the epithelioid subtype. Surgery is recommended for resectable cases with radiotherapy being indicated in neoadjuvant, adjuvant, and palliative settings along with systemic treatment. This review article provides an overview of epidemiology, etiology, clinical manifestations, diagnostic approaches (including immunohistochemical and genetic markers), staging, and multidisciplinary approaches to current treatment for malignant pleural mesothelioma using surgery, chemotherapy, immunotherapy, and radiotherapy. It also sheds light on some recent studies (EMPHACIS, CALGB30901, Checkmate-743, etc.) that have led to significant developments in recent years with clinically meaningful results.

Evaluation of the Treatment Planning and Delivery for Hip Implant Cases on Tomotherapy.

Purpose: The metal present in the implant creates artifacts during the treatment simulation, which impacts the treatment planning and delivery of the prescribed dose to the target and sparing normal tissues. This retrospective study evaluated the uncertainties in the planning and delivery of doses for prosthesis cases with dedicated phantom. Materials and Methods: In this retrospective study, 11 patients with a hip prosthesis having cervix carcinoma were selected. Two treatment plans were generated on treatment planning system (TPS) for each case. Plan_No_Res was without any beam restriction, and Plan_exit_only was the plan with restricted beam entry through the metallic implant. An indigenous phantom was utilized to verify the accuracy of the treatment. In the phantom, some groves were present, which could be filled by implants that mimic the patient's geometries, like left, right and bilateral femur implants. The delivered doses were recorded using optically stimulated luminescence dosimeters (OSLDs), which were placed at different positions in the phantom. The plans were further calculated using megavoltage computed tomography (MVCT) scans acquired during treatment. Results: The patient data showed no significant dose changes between the two planning methods. The treatment time increases from 412.18 ± 86.65 to 427.36 ± 104.80 with P = 0.03 for Plan_No_Res and Plan_exit_only, respectively. The difference between planned and delivered doses of various points across phantom geometries was within ± 9.5% in each case as left, right, and bilateral implant. The variations between OSLDs and MVCT calculated doses were also within ± 10.8%. Conclusion: The study showed the competency of tomotherapy planning for hip prosthesis cases. The phantom measurements demonstrate the errors in dosimetry near the implant material, suggesting the need for precise methods to deal with artifact-related issues.

Novel technique for laparoscopic common bile duct exploration using flexible videobronchoscope to study on clinical outcomes of single-stage (laparoscopic cholecystectomy and laparoscopic common bile duct exploration) versus dual-stage (endoscopic retrograde cholangiopancreatography followed by laparoscopic cholecystectomy) for cholelithiasis with choledocholithiasis - Prospective study in a tertiary care centre (BRACE study - BRonchoscope Assisted Common bile duct Exploration Study).

INTRODUCTION: This study aimed to study on clinical outcomes of single-stage (laparoscopic cholecystectomy [LC] and laparoscopic common bile duct [CBD] exploration using flexible videobronchoscope) versus dual-stage (endoscopic retrograde cholangiopancreatography followed by laparoscopic cholecystectomy) for cholelithiasis with choledocholithiasis-prospective study in a tertiary care centre (BRACE STUDY-Bronchoscope-Assisted CBD Exploration [CBDE] Study). PATIENTS AND METHODS: Between April 2022 and April 2023, patients who underwent LC with laparoscopic CBDE and endoscopic retrograde cholangiopancreatography (ERCP) followed by LC participated in this single-centre prospective research. The Institute Ethics Committee granted its approval after receiving an ethical review. The primary endpoint of the proposed research was the removal of the gall bladder and CBD stones. The secondary outcomes studied were complications using the Clavien-Dindo score, cost-effectiveness, patient satisfaction score and post-procedure duration of hospital stay. RESULTS: A total of 168 patients were included in the study. The success rate of LC with laparoscopic CBD exploration using a flexible videobronchoscope (Group 1) was significantly higher as compared to ERCP f/b LC (Group 2) (96.4% vs. 84.5%, P value = 0.02). Out of the 84 patients in Group 1, direct choledochotomies were performed on 83 of them. Group 1 had a considerably shorter hospital stay (4.6 ± 2.4 vs. 5.3 ± 6.2 days; P = 0.03). Both the cost ( P = 0.002) and the number of procedures per patient ( P < 0.001) were considerably higher in Group 2. Major complications (Clavien-Dindo grade 3 and above) were significantly higher in Group 2 ( P = 0.04). Patient satisfaction in Group 1 scored more favourably than those in Group 2 (2.26 ± 0.3 vs. 1.92 ± 0.7; P = 0.006). CONCLUSION: For concurrent gall bladder and CBD stones, single-stage management by LCBDE using a flexible videobronchoscope has a significantly better primary outcome and lower major complications than dual-stage management. The single-stage strategy also has advantages in terms of a shorter hospital stay, the need for fewer procedures, cost efficiency and patient satisfaction.

Minimal Residual Disease in the Management of B-Cell Acute Lymphoblastic Leukemia: A Systematic Review of Studies from Indian Settings.

Minimal residual disease (MRD) has become an essential tool in the management of B-cell acute lymphoblastic leukemia (B-ALL) and aids in tailoring treatment strategies to suit specific patient needs. Although much progress has been made in this area, there is limited data on the use of MRD in the Indian context. Our objective was to identify relevant literature that discusses the utility of MRD in the management of B-cell ALL in adolescents and young adults (AYA) and adults in Indian settings. A systematic search and screening of articles were performed using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The primary data source was PubMed followed by Google Scholar for articles and conference proceedings. Of the 254 records screened, 24 records were retained for analysis. MRD monitoring had a significant role in the management of AYA/adult B-cell ALL patients. Variability of results was observed across these studies with respect to methods, techniques, and use. However, these studies evidenced and validated the importance of MRD assessment in risk-adapted management of B-cell ALL and highlighted the need for optimization. The advances in MRD diagnostics and applications are yet to be tested and adopted in Indian settings. Hence, there is a need for in-depth research to develop and optimize approaches for calibrating country-specific management strategies. The potential role of MRD assessments in anticipating relapse or treatment failures warrants more attention for the preemptive positioning of novel strategies involving immunotherapies.

Laparoscopic subtotal cholecystectomy in difficult gallbladder: Our experience in a tertiary care center.

Backgrounds/Aims: Open cholecystectomy is becoming obsolete and laparoscopic cholecystectomy has become the treatment of choice in gallstone diseases. Difficult gallbladders are encountered whenever there is a frozen calot's triangle, obliterated cystic plate, or both. Rather than converting to open procedure, there has been a growing preference for laparoscopic subtotal cholecystectomy (LSC) during difficult gallbladders. This study aimed to assess the advantages, indications, and viability of LSC in difficult gallbladders. Methods: The study included patients undergoing laparoscopic cholecystectomy in NIMS Hospital, Jaipur, from January 2021 to January 2023. Data of the patients who underwent LSC for difficult gallbladders included demographics, comorbidities, operative time, conversion to open cholecystectomy, length of hospital stay, and complications. LSC was classified into three types depending on the part of the gallbladder remnant. Results: A total of 728 patients underwent laparoscopic cholecystectomy. Among them, 41 patients (5.6%) were attempted for LSC. However, one patient was converted to an open procedure and the rest 40 underwent LSC. LSC was divided into 3 types, 4 patients underwent LSC type I, 34 patients underwent type II, and 2 patients type III. The average operating time and postoperative length of hospital stay were 86.2 minutes and 2.1 days, respectively. Two patients had surgical site infection. No patient had a bile leak and none required intensive care unit care. Conclusions: LSC is a safe and feasible option for use in difficult gallbladders.

Perioperative Treatment Strategies in EGFR-Mutant Early-Stage NSCLC: Current Evidence and Future Challenges.

Treatment with 3 years of adjuvant osimertinib is considered a new standard in patients with completely resected stage I to IIIA NSCLC harboring a common sensitizing EGFR mutation. This therapeutic approach significantly prolonged the disease-free survival and the overall survival versus placebo and revealed a significant role in preventing the occurrence of brain metastases. However, many unanswered questions remain, including the optimal duration of this therapy, whether all patients benefit from adjuvant osimertinib, and the role of adjuvant chemotherapy in this population. Indeed, there is a renewed interest in neoadjuvant strategies with targeted therapies in resectable NSCLC harboring oncogenic drivers. In light of these considerations, we discuss the past and current treatment options, and the clinical challenges that should be addressed to optimize the treatment outcomes in this patient population.

Efficacy of different respiratory supports to prevent hypoxia during flexible bronchoscopy in patients of COPD: a triple-arm, randomised controlled trial.

BACKGROUND: Patients with chronic-obstructive-pulmonary-disease (COPD) undergo bronchoscopy for various reasons, and are at relatively higher risk of complications. This study evaluated the efficacy of non-invasive ventilation (NIV) and high-flow-oxygen-therapy (HFOT) compared with conventional-oxygen-therapy (COT) in patients with COPD undergoing bronchoscopy, to prevent hypoxia. METHODS: It was a triple-arm, open-label, randomised controlled trial. Ninety patients with COPD were randomly assigned into three intervention arms in 1:1:1 ratio. The incidence of hypoxia, lowest recorded oxygen saturation measured by plethysmography (SpO2), ECG, patient vitals and comfort levels were assessed. RESULTS: Mean age of the study population was 61.71±7.5 years. Out of 90 cases enrolled, 51, 34 and 5 were moderate, severe and very-severe COPD, respectively, as per GOLD (Global Initiative for Chronic Obstructive Lung Disease) classification. Rest of the baseline characteristics were similar. SpO2 during flexible bronchoscopy (FB) was lowest in COT group (COT: 87.03±5.7% vs HFOT: 95.57±5.0% vs NIV: 97.40±1.6%, p<0.001). Secondary objectives were similar except respiratory-rate (breaths-per-minute) which was highest in COT group (COT: 20.23±3.1 vs HFOT: 18.57±4.1 vs NIV: 16.80±1.9, p<0.001). Whereas post FB partial of oxygen in arterial blood was highest in NIV group (NIV: 84.27±21.6 mm Hg vs HFOT: 69.03±13.6 mm Hg vs COT: 69.30±11.9 mm Hg, p<0.001). Post FB partial pressure of carbon dioxide in arterial blood was similar in the three arms. Operator's ease-of-performing-procedure was least in the NIV group as assessed with Visual Analogue Scale (p<0.01). A higher number of NIV group participants reported nasal pain as compared with the other two arms (p<0.01). CONCLUSION: NIV and HFOT are superior to COT in preventing hypoxia during bronchoscopy, but NIV is associated with poor patient-tolerance and inferior operator's ease of doing procedure. TRIAL REGISTRATION NUMBER: CTRI/2021/03/032190.

SnapEnsemFS: a snapshot ensembling-based deep feature selection model for colorectal cancer histological analysis.

Colorectal cancer is the third most common type of cancer diagnosed annually, and the second leading cause of death due to cancer. Early diagnosis of this ailment is vital for preventing the tumours to spread and plan treatment to possibly eradicate the disease. However, population-wide screening is stunted by the requirement of medical professionals to analyse histological slides manually. Thus, an automated computer-aided detection (CAD) framework based on deep learning is proposed in this research that uses histological slide images for predictions. Ensemble learning is a popular strategy for fusing the salient properties of several models to make the final predictions. However, such frameworks are computationally costly since it requires the training of multiple base learners. Instead, in this study, we adopt a snapshot ensemble method, wherein, instead of the traditional method of fusing decision scores from the snapshots of a Convolutional Neural Network (CNN) model, we extract deep features from the penultimate layer of the CNN model. Since the deep features are extracted from the same CNN model but for different learning environments, there may be redundancy in the feature set. To alleviate this, the features are fed into Particle Swarm Optimization, a popular meta-heuristic, for dimensionality reduction of the feature space and better classification. Upon evaluation on a publicly available colorectal cancer histology dataset using a five-fold cross-validation scheme, the proposed method obtains a highest accuracy of 97.60% and F1-Score of 97.61%, outperforming existing state-of-the-art methods on the same dataset. Further, qualitative investigation of class activation maps provide visual explainability to medical practitioners, as well as justifies the use of the CAD framework in screening of colorectal histology. Our source codes are publicly accessible at: https://github.com/soumitri2001/SnapEnsemFS .

Management of B-cell lineage acute lymphoblastic leukemia: expert opinion from an Indian panel via Delphi consensus method.

Introduction: Currently, there are no guidelines for the management of B-cell lineage acute lymphoblastic leukemia (B-ALL) from an Indian perspective. The diagnostic workup, monitoring, and treatment of B-ALL vary among different physicians and institutes. Objective: To develop evidence-based practical consensus recommendations for the management of B-ALL in Indian settings. Methods: Modified Delphi consensus methodology was considered to arrive at a consensus. An expert scientific committee of 15 experts from India constituted the panel. Clinically relevant questions belonging to three major domains were drafted for presentation and discussion: (i) diagnosis and risk assignment; (ii) frontline treatment; and (iii) choice of therapy (optimal vs. real-world practice) in relapsed/refractory (R/R) settings. The questionnaire was shared with the panel members through an online survey platform. The level of consensus was categorized into high (≥ 80%), moderate (60%-79%), and no consensus (< 60%). The process involved 2 rounds of discussion and 3 rounds of Delphi survey. The questions that received near or no consensus were discussed during virtual meetings (Delphi rounds 1 and 2). The final draft of the consensus was emailed to the panel for final review. Results: Experts recommended morphologic assessment of peripheral blood or bone marrow, flow cytometric immunophenotyping, and conventional cytogenetic analysis in the initial diagnostic workup. Berlin-Frankfurt-Münster (BFM)-based protocol is the preferred frontline therapy in pediatric and adolescent and young adult patients with B-ALL. BFM/German Multicenter Study Group for Adult Acute Lymphoblastic Leukemia-based regimen is suggested in adult patients with B-ALL. Immunotherapy (blinatumomab or inotuzumab ozogamicin) followed by allogeneic hematopoietic cell transplantation (allo-HCT) is the optimal choice of therapy that would yield the best outcomes if offered in the first salvage in patients with R/R B-ALL. In patients with financial constraints or prior allo-HCT (real-world practice) at first relapse, standard-intensive chemotherapy followed by allo-HCT may be considered. For subsequent relapses, chimeric antigen receptor T-cell therapy or palliative care was suggested as the optimal choice of therapy. Conclusion: This expert consensus will offer guidance to oncologists/clinicians on the management of B-ALL in Indian settings.

Dynamic assessment of oropharynx with ultrasonography as a screening tool for obstructive sleep apnea.

Ultrasonography is an easily available and portable tool to assess the dynamic changes in the upper airway and surrounding soft tissue. This study aimed to evaluate the utility of oropharynx ultrasonography as a screening tool for obstructive sleep apnea (OSA). The study sequentially enrolled overweight individuals (body mass index >25 kg/m2 ) and subjected them to OSA screening tools (Berlin questionnaire, Epworth Sleepiness Scale and STOP-Bang scores), ultrasonography of the oropharynx followed by overnight polysomnography. A total of 30 healthy individuals were also recruited as controls. Detailed dynamic and static ultrasonography measurements of the oropharynx and surrounding tissue were done. The diagnostic ability of various ultrasonography parameters to detect OSA was determined using receiver operating characteristic curve analysis. A total of 63 subjects were enrolled, with 33 in the OSA group and 30 in non-OSA overweight group. All baseline characteristics were similar in the two groups. Except for the dynamic measurements of oropharynx (Retropalatal% change-inspiration, retropalatal% change-Muller manoeuvre, retroglossal% change-inspiration, and retroglossal% change-Muller manoeuvre) all other parameters were similar in the OSA and non-OSA overweight subjects. The area under the receiver operating characteristic curve was highest for retropalatal% change-inspiration: 0.989, followed by retropalatal% change-Muller manoeuvre: 0.988. Both were also significant predictors of OSA with odds ratios of 0.338 (p = 0.003; 95% confidence interval [CI] 0.164-0.696) and 0.346 (p = 0.018; 95% CI 0.143-0.837), respectively. Ultrasonography provides a near complete picture of the dynamic changes and collapsibility of the oropharynx and can be an effective tool in screening for OSA.

Classification of Mental Stress from Wearable Physiological Sensors Using Image-Encoding-Based Deep Neural Network.

The human body is designed to experience stress and react to it, and experiencing challenges causes our body to produce physical and mental responses and also helps our body to adjust to new situations. However, stress becomes a problem when it continues to remain without a period of relaxation or relief. When a person has long-term stress, continued activation of the stress response causes wear and tear on the body. Chronic stress results in cancer, cardiovascular disease, depression, and diabetes, and thus is deeply detrimental to our health. Previous researchers have performed a lot of work regarding mental stress, using mainly machine-learning-based approaches. However, most of the methods have used raw, unprocessed data, which cause more errors and thereby affect the overall model performance. Moreover, corrupt data values are very common, especially for wearable sensor datasets, which may also lead to poor performance in this regard. This paper introduces a deep-learning-based method for mental stress detection by encoding time series raw data into Gramian Angular Field images, which results in promising accuracy while detecting the stress levels of an individual. The experiment has been conducted on two standard benchmark datasets, namely WESAD (wearable stress and affect detection) and SWELL. During the studies, testing accuracies of 94.8% and 99.39% are achieved for the WESAD and SWELL datasets, respectively. For the WESAD dataset, chest data are taken for the experiment, including the data of sensor modalities such as three-axis acceleration (ACC), electrocardiogram (ECG), body temperature (TEMP), respiration (RESP), etc.

New Genomic Regions Identified for Resistance to Spot Blotch and Terminal Heat Stress in an Interspecific Population of Triticum aestivum and T. spelta.

Wheat is one of the most widely grown and consumed food crops in the world. Spot blotch and terminal heat stress are the two significant constraints mainly in the Indo-Gangetic plains of South Asia. The study was undertaken using 185 recombinant lines (RILs) derived from the interspecific hybridization of 'Triticum aestivum (HUW234) × T. spelta (H+26)' to reveal genomic regions associated with tolerance to combined stress to spot blotch and terminal heat. Different physiological (NDVI, canopy temperature, leaf chlorophyll) and grain traits (TGW, grain size) were observed under stressed (spot blotch, terminal heat) and non-stressed environments. The mean maturity duration of RILs under combined stress was reduced by 12 days, whereas the normalized difference vegetation index (NDVI) was 46.03%. Similarly, the grain size was depleted under combined stress by 32.23% and thousand kernel weight (TKW) by 27.56% due to spot blotch and terminal heat stress, respectively. The genetic analysis using 6734 SNP markers identified 37 significant loci for the area under the disease progress curve (AUDPC) and NDVI. The genome-wide functional annotation of the SNP markers revealed gene functions such as plant chitinases, NB-ARC and NBS-LRR, and the peroxidase superfamily Cytochrome P450 have a positive role in the resistance through a hypersensitive response. Zinc finger domains, cysteine protease coding gene, F-box protein, ubiquitin, and associated proteins, play a substantial role in the combined stress of spot blotch and terminal heat in bread wheat, according to genomic domains ascribed to them. The study also highlights T. speltoides as a source of resistance to spot blotch and terminal heat tolerance.

MTRRE-Net: A deep learning model for detection of breast cancer from histopathological images.

Histopathological image classification has become one of the most challenging tasks among researchers due to the fine-grained variability of the disease. However, the rapid development of deep learning-based models such as the Convolutional Neural Network (CNN) has propelled much attentiveness to the classification of complex biomedical images. In this work, we propose a novel end-to-end deep learning model, named Multi-scale Dual Residual Recurrent Network (MTRRE-Net), for breast cancer classification from histopathological images. This model introduces a contrasting approach of dual residual block combined with the recurrent network to overcome the vanishing gradient problem even if the network is significantly deep. The proposed model has been evaluated on a publicly available standard dataset, namely BreaKHis, and achieved impressive accuracy in overcoming state-of-the-art models on all the images considered at various magnification levels.

Essential Role of NLRP3 Inflammasome in Mediating IL-1ß Production and the Pathobiology of Staphylococcus aureus Endophthalmitis.

Staphylococcal endophthalmitis is one of the leading causes of blindness following ocular surgery and trauma. Dysregulated inflammation during bacterial endophthalmitis causes host-induced inflammatory damage and vision loss if it remains unchecked. Emerging evidence indicates that inflammasome plays a critical role in regulating innate immunity in various infectious and inflammatory diseases. However, the role of the inflammasome in endophthalmitis remains elusive. Here, using a mouse model of Staphylococcus (S) aureus endophthalmitis, we show that NLRP3/ASC/Caspase-1 signaling regulates IL-1ß production in endophthalmitis. We also show that S. aureus and its cell wall components and toxins induce the activation of the NLRP3 inflammasome complex in mouse eyes. Moreover, we found that both infiltrating neutrophils and retinal microglia contribute toward NLRP3 activation and IL-1ß production in S. aureus-infected eyes. Furthermore, our data using NLRP3-/- and IL-1ß-/- mice revealed that NLRP3 and IL-1ß deficiency leads to increased intraocular bacterial burden and retinal tissue damage. Altogether, our study demonstrated an essential role of NLRP3 inflammasome activation in regulating innate immune responses in bacterial endophthalmitis.

DRDA-Net: Dense residual dual-shuffle attention network for breast cancer classification using histopathological images.

Breast cancer is caused by the uncontrolled growth and division of cells in the breast, whereby a mass of tissue called a tumor is created. Early detection of breast cancer can save many lives. Hence, many researchers worldwide have invested considerable effort in developing robust computer-aided tools for the classification of breast cancer using histopathological images. For this purpose, in this study we designed a dual-shuffle attention-guided deep learning model, called the dense residual dual-shuffle attention network (DRDA-Net). Inspired by the bottleneck unit of the ShuffleNet architecture, in our proposed model we incorporate a channel attention mechanism, which enhances the model's ability to learn the complex patterns of images. Moreover, the model's densely connected blocks address both the overfitting and the vanishing gradient problem, although the model is trained on a substantially small dataset. We have evaluated our proposed model on the publicly available BreaKHis dataset and achieved classification accuracies of 95.72%, 94.41%, 97.43% and 98.1% on four different magnification levels i.e., 40x, 1000x, 200x, 400x respectively which proves the supremacy of the proposed model. The relevant code of the proposed DRDA-Net model can be foundt at: https://github.com/SohamChattopadhyayEE/DRDA-Net.

To Study the Impact of Different Optimization Methods on Intensity-Modulated Radiotherapy and Volumetric-Modulated Arc Therapy Plans for Hip Prosthesis Patients.

Purpose: To study the impact of different optimization methods in dealing with metallic hip implant using intensity-modulated radiotherapy (IMRT) and volumetric-modulated arc therapy (VMAT) techniques. Materials and Methods: A cohort of 16 patients having metallic implants was selected for the study. Three sets of IMRT and VMAT plans were generated. Set 1 IMRT (IM_Base), VMAT (VM_Base) without any restrictions on beam entry and exit, set 2 (IM_ENT and VM_ENT) optimizer restricts the beam entry and set 3 (IM_EXT+ENT), neither entry nor exit doses were allowed toward the metallic implant. Results: There was no significant difference in target (D95%) and organ-at-risk doses between IM_Base and IM_ENT. There were significant (P = 0.002) improvements in planning target volume (PTV) V95% and homogeneity from IM_EXT+ENT to IM_ENT. There was no significant difference in plan quality between VM_Base and VM_ENT. There were significant (P = 0.005) improvements in PTV, V95%, homogeneity from VM_EXT+ENT to VM_ENT. V40Gy, V30Gy for bladder, rectum, bowel, and bowel maximum dose decreases significantly (P < 0.005) in IM_ENT compared to IM_EXT+ENT, but not significant for VMAT plans. Similarly, there was a significant decrease in dose spill outside target (P < 0.05) comparing 40%, 50%, 60%, and 70% dose spills for IM_ENT compared to IM_EXT+ENT, but variations among VMAT plans are insignificant. VMAT plans were always superior to IMRT plans for the same optimization methods. Conclusion: The best approach is to plan hip prosthesis cases with blocked entry of radiation beam for IMRT and VMAT. The VMAT plans had more volumetric coverage, fewer hotspots, and lesser heterogeneity.

A Tri-Stage Wrapper-Filter Feature Selection Framework for Disease Classification.

In machine learning and data science, feature selection is considered as a crucial step of data preprocessing. When we directly apply the raw data for classification or clustering purposes, sometimes we observe that the learning algorithms do not perform well. One possible reason for this is the presence of redundant, noisy, and non-informative features or attributes in the datasets. Hence, feature selection methods are used to identify the subset of relevant features that can maximize the model performance. Moreover, due to reduction in feature dimension, both training time and storage required by the model can be reduced as well. In this paper, we present a tri-stage wrapper-filter-based feature selection framework for the purpose of medical report-based disease detection. In the first stage, an ensemble was formed by four filter methods-Mutual Information, ReliefF, Chi Square, and Xvariance-and then each feature from the union set was assessed by three classification algorithms-support vector machine, naïve Bayes, and k-nearest neighbors-and an average accuracy was calculated. The features with higher accuracy were selected to obtain a preliminary subset of optimal features. In the second stage, Pearson correlation was used to discard highly correlated features. In these two stages, XGBoost classification algorithm was applied to obtain the most contributing features that, in turn, provide the best optimal subset. Then, in the final stage, we fed the obtained feature subset to a meta-heuristic algorithm, called whale optimization algorithm, in order to further reduce the feature set and to achieve higher accuracy. We evaluated the proposed feature selection framework on four publicly available disease datasets taken from the UCI machine learning repository, namely, arrhythmia, leukemia, DLBCL, and prostate cancer. Our obtained results confirm that the proposed method can perform better than many state-of-the-art methods and can detect important features as well. Less features ensure less medical tests for correct diagnosis, thus saving both time and cost.

Integrative metabolomics and transcriptomics identifies itaconate as an adjunct therapy to treat ocular bacterial infection.

The eye is highly susceptible to inflammation-mediated tissue damage evoked during bacterial infection. However, mechanisms regulating inflammation to protect the eye remain elusive. Here, we used integrated metabolomics and transcriptomics to show that the immunomodulatory metabolite itaconate and immune-responsive gene 1 (Irg1) are induced in bacterial (Staphylococcus aureus)-infected mouse eyes, bone-marrow-derived macrophages (BMDMs), and Müller glia. Itaconate levels are also elevated in the vitreous of patients with bacterial endophthalmitis. Irg1 deficiency in mice led to increased ocular pathology. Conversely, intraocular administration of itaconate protects both Irg1-/- and wild-type mice from bacterial endophthalmitis by reducing inflammation, bacterial burden, and preserving retinal architecture and visual function. Notably, itaconate exerts synergistic effects with antibiotics. The protective, anti-inflammatory effects of itaconate are mediated via activation of NRF2/HO-1 signaling and inhibition of NLRP3 inflammasome. Collectively, our study demonstrates the Irg1/itaconate axis is a regulator of intraocular inflammation and provides evidence for using itaconate, along with antibiotics, to treat bacterial infections.