Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review.

Background: The field of precision medicine endeavors to transform the healthcare industry by advancing individualised strategies for diagnosis, treatment modalities, and predictive assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD). Methods: We conducted a comprehensive scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Our search strategy involved combining key terms related to CVD and AI using the Boolean operator AND. In August 2023, we conducted an extensive search across reputable scholarly databases including Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Web of Science, and arXiv to gather relevant academic literature on personalised medicine for CVD. Subsequently, in January 2024, we extended our search to include internet search engines such as Google and various CVD websites. These searches were further updated in March 2024. Additionally, we reviewed the reference lists of the final selected research articles to identify any additional relevant literature. Findings: A total of 2307 records were identified during the process of conducting the study, consisting of 564 entries from external sites like arXiv and 1743 records found through database searching. After 430 duplicate articles were eliminated, 1877 items that remained were screened for relevancy. In this stage, 1241 articles remained for additional review after 158 irrelevant articles and 478 articles with insufficient data were removed. 355 articles were eliminated for being inaccessible, 726 for being written in a language other than English, and 281 for not having undergone peer review. Consequently, 121 studies were deemed suitable for inclusion in the qualitative synthesis. At the intersection of CVD, AI, and precision medicine, we found important scientific findings in our scoping review. Intricate pattern extraction from large, complicated genetic datasets is a skill that AI algorithms excel at, allowing for accurate disease diagnosis and CVD risk prediction. Furthermore, these investigations have uncovered unique genetic biomarkers linked to CVD, providing insight into the workings of the disease and possible treatment avenues. The construction of more precise predictive models and personalised treatment plans based on the genetic profiles of individual patients has been made possible by the revolutionary advancement of CVD risk assessment through the integration of AI and genomics. Interpretation: The systematic methodology employed ensured the thorough examination of available literature and the inclusion of relevant studies, contributing to the robustness and reliability of the study's findings. Our analysis stresses a crucial point in terms of the adaptability and versatility of AI solutions. AI algorithms designed in non-CVD domains such as in oncology, often include ideas and tactics that might be modified to address cardiovascular problems. Funding: No funding received.

Deep learning approach for cardiovascular disease risk stratification and survival analysis on a Canadian cohort.

The quantification of carotid plaque has been routinely used to predict cardiovascular risk in cardiovascular disease (CVD) and coronary artery disease (CAD). To determine how well carotid plaque features predict the likelihood of CAD and cardiovascular (CV) events using deep learning (DL) and compare against the machine learning (ML) paradigm. The participants in this study consisted of 459 individuals who had undergone coronary angiography, contrast-enhanced ultrasonography, and focused carotid B-mode ultrasound. Each patient was tracked for thirty days. The measurements on these patients consisted of maximum plaque height (MPH), total plaque area (TPA), carotid intima-media thickness (cIMT), and intraplaque neovascularization (IPN). CAD risk and CV event stratification were performed by applying eight types of DL-based models. Univariate and multivariate analysis was also conducted to predict the most significant risk predictors. The DL's model effectiveness was evaluated by the area-under-the-curve measurement while the CV event prediction was evaluated using the Cox proportional hazard model (CPHM) and compared against the DL-based concordance index (c-index). IPN showed a substantial ability to predict CV events (p < 0.0001). The best DL system improved by 21% (0.929 vs. 0.762) over the best ML system. DL-based CV event prediction showed a ~ 17% increase in DL-based c-index compared to the CPHM (0.86 vs. 0.73). CAD and CV incidents were linked to IPN and carotid imaging characteristics. For survival analysis and CAD prediction, the DL-based system performs superior to ML-based models.

Efficacy and safety of a 4-week course of repeated subcutaneous ketamine injections for treatment-resistant depression (KADS study): randomised double-blind active-controlled trial.

BACKGROUND: Prior trials suggest that intravenous racemic ketamine is a highly effective for treatment-resistant depression (TRD), but phase 3 trials of racemic ketamine are needed. AIMS: To assess the acute efficacy and safety of a 4-week course of subcutaneous racemic ketamine in participants with TRD. Trial registration: ACTRN12616001096448 at www.anzctr.org.au. METHOD: This phase 3, double-blind, randomised, active-controlled multicentre trial was conducted at seven mood disorders centres in Australia and New Zealand. Participants received twice-weekly subcutaneous racemic ketamine or midazolam for 4 weeks. Initially, the trial tested fixed-dose ketamine 0.5 mg/kg versus midazolam 0.025 mg/kg (cohort 1). Dosing was revised, after a Data Safety Monitoring Board recommendation, to flexible-dose ketamine 0.5-0.9 mg/kg or midazolam 0.025-0.045 mg/kg, with response-guided dosing increments (cohort 2). The primary outcome was remission (Montgomery-Åsberg Rating Scale for Depression score ≤10) at the end of week 4. RESULTS: The final analysis (those who received at least one treatment) comprised 68 in cohort 1 (fixed-dose), 106 in cohort 2 (flexible-dose). Ketamine was more efficacious than midazolam in cohort 2 (remission rate 19.6% v. 2.0%; OR = 12.1, 95% CI 2.1-69.2, P = 0.005), but not different in cohort 1 (remission rate 6.3% v. 8.8%; OR = 1.3, 95% CI 0.2-8.2, P = 0.76). Ketamine was well tolerated. Acute adverse effects (psychotomimetic, blood pressure increases) resolved within 2 h. CONCLUSIONS: Adequately dosed subcutaneous racemic ketamine was efficacious and safe in treating TRD over a 4-week treatment period. The subcutaneous route is practical and feasible.

A study of subcutaneous emphysema, factors contributing to its development, resolution and management with different modalities.

Subcutaneous emphysema is defined as an escape of air in subcutaneous tissue. It is one of the most common complications after inter-costal chest tube drainage. Subcutaneous emphysema is usually benign requiring no specific treatment, but extensive subcutaneous emphysema can be uncomfortable and alarming for the patient. It can rarely lead to airway compromise, respiratory failure and death. Factors leading to its development, following chest tube insertion and methods of management, have not been extensively studied and published. This was an analytical study done over a period of two years, on indoor patients who developed subcutaneous emphysema. These cases were managed using four different modalities and were analyzed for various factors contributing to the development, severity, and resolution of subcutaneous emphysema. Results of this study highlight that the cases of hydropneumothorax and secondary pneumothorax were significantly more predisposed to the development of severe subcutaneous emphysema (following intercostal chest tube insertion) and large air leak as compared to others. Larger air leak develops higher grades of subcutaneous emphysema. The average time for resolution of subcutaneous emphysema was similar among the different modalities of management compared in the study.

A case report of Ovophis monitcola (Mountain pit-viper) envenoming in northeastern India resulting in prolonged coagulopathy.

India is home to a diverse spectrum of medically-significant snakes accounting for one of the world's largest burdens of envenoming, morbidity and mortality. Indian polyspecific antivenom is derived from the venom of four snake species (Daboia russelii, Echis carinatus, Naja naja and Bungarus caeruleus), considered to be responsible for the majority of snakebite morbidity and mortality in India. The treatment of envenoming from other less-commonly encountered venomous snake species can be challenging. In this report, we describe the case of a 32-year-old male who presented with local swelling and coagulopathy following a bite from Ovophis monitcola (mountain pit-viper) in Nagaland, Northeast India. Local and systemic envenoming, failed to respond to Indian polyspecific antivenom and venom-induced consumption coagulopathy, confirmed by bedside and laboratory-based clotting assays, persisted for more than three weeks. Remote consultation with a national-level Poison Control Centre helped establish the responsible snake species and guide appropriate medical management.

A comparison of efficacy of parenteral and perineural dexmedetomidine with 0.25% ropivacaine for post-thyroidectomy analgesia using bilateral superficial cervical plexus block.

Background and Aims: Opioids are conventionally used for post-thyroidectomy pain, regional anesthesia is becoming popular due to its feasibility and efficacy in minimizing use of opioids and hence its side effects. This study compared analgesic efficacy of bilateral superficial cervical plexus block (BSCPB) using perineural and parenteral dexmedetomidine with 0.25% ropivacaine in thyroidectomy patients. Material and Methods: In this double-blind study, 60 American Society of Anesthesiologists (ASA) physical status I and II thyroidectomy patients, aged 18-65 years were randomized into two groups. Group A (n = 30) received BSCPB with 0.25% ropivacaine, 10 mL on each side with dexmedetomidine 0.5 µg/kg IV infusion. Group B (n = 30) received 0.25% ropivacaine plus dexmedetomidine 0.5 µg/kg, ten mL on each side. Duration of analgesia by measuring pain visual analog scores (VAS), total dose of analgesic requirement, Haemodynamics parameters and adverse events were recorded for 24 h. Categorical variables were analyzed using Chi-square test and continuous variables were computed as mean with standard deviation and analyzed using independent sample t-test. Mann-Whitney U test was used for analysis of ordinal variables. Results: Time to rescue analgesia was longer in Group B (18.6 ± 3.27 h) as compared to Group A (10.2 ± 2.11 h) (P < 0.001). Total analgesic dose required was also found to be lesser in Group B (50.83 ± 20.37 mg) as compared to Group A (73.33 ± 18.27 mg) (P < 0.001). No significant hemodynamic changes or side effects were observed in both groups; (P > 0.05). Conclusion: Perineural dexmedetomidine with ropivacaine in BSCPB significantly prolonged the duration of analgesia with reduced rescue analgesic requirement.

Post-operative analgesic efficacy of 0.25% ropivacaine with dexmedetomidine versus dexamethasone as an adjuvant in bilateral superficial cervical plexus block for thyroidectomy under general anaesthesia - A comparative randomized clinical study.

Background and Aims: The bilateral superficial cervical plexus block (BSCPB) is efficacious for post-operative analgesia in thyroid surgeries. We assessed the analgesic efficacy of dexmedetomidine and dexamethasone administered as adjuvants with 0.25 percent ropivacaine in BSCPB for thyroidectomy under general anaesthesia with regard to the duration of analgesia, total amount of rescue analgesic requirement, changes in intra- and post-operative haemodynamic parameters, VAS scores, and adverse events, if any. Methods: A prospective double-blind trial was planned with 80 adults undergoing thyroidectomy, randomized into two equal groups and given BSCPB with 20 ml 0.25% ropivacaine with adjuvants as either dexmedetomidine 50mg (group A) or dexamethasone 4mg (group B), 10 ml on each side, after the induction of general anaesthesia. Post-operative pain was monitored using the visual analog scale and the duration of analgesia was measured by time to first rescue analgesia. Post-operative haemodynamics and any adverse events were recorded. Results: The mean duration of analgesia was slightly prolonged in group A but statistically non-significant as compared to group B (1037 ± 97 vs. 1004 ± 122 minutes; P0.18). The post-operative median VAS scores and vital parameters were relatively comparable for both groups (P > 0.05) for the first 24h. There was a significant reduction in the incidence of PONV (P < 0.05) in group B. Conclusion: Although dexamethasone offers a slight advantage of decreased incidence of PONV, BSCPB using ropivacaine with either dexmedetomidine or dexamethasone as an adjuvant imparted adequate analgesia with stable haemodynamics and may be used as a pre-emptive analgesic technique in thyroid surgeries.

Risk factors for pulmonary mucormycosis in subjects with diabetes mellitus-A case-control study.

BACKGROUND: Factors associated with pulmonary mucormycosis (PM) among subjects with diabetes mellitus (DM) remain unclear. Following the coronavirus disease (COVID-19)-associated mucormycosis outbreak in India, specific environmental exposures (especially cattle dung exposure) were proposed as possible aetiology. We hypothesized that environmental factors are associated with PM. We compared subjects with DM with (cases) and without PM (controls). METHODS: In this case-control study, for each PM case, we included five unmatched diabetic controls (hospital [n = 2], community [n = 3]) without PM. We collected information on demography, COVID-19 infection, glycated haemoglobin% (HbA1c), the type of house (pucca vs. kutcha) where the participants reside, and other environmental factors. The primary exposure tested was cattle dung exposure (CDE; using cattle dung cakes as fuel or cattle handling). We performed a multivariate logistic regression to explore factors associated with PM and report the association as an adjusted odds ratio (OR) with 95% confidence intervals (CI). RESULTS: We enrolled 39 PM cases and 199 controls (hospital [n = 80], community [n = 119]). CDE (OR 0.68, 95% CI [0.14-3.31]; p = 0.63) was not associated with increased PM in DM. We found male sex (OR 4.07, 95% CI [1.16-14.31]), higher HbA1c (OR 1.51, 95% CI [1.18-16.32]), COVID-19 (OR 28.25, 95% CI [7.02-113.6]) and residence at kutcha house (OR 4.84, 95% CI [1.33-17.52]) associated with PM. CONCLUSION: Cattle dung exposure was not associated with PM in subjects with DM. Instead, male sex, poor glycaemic control, COVID-19 and the type of housing were associated with pulmonary mucormycosis.

Real-world five-year outcomes of FlexyRap® cobalt-chromium rapamycin-eluting stents with biodegradable polymer in patients with de-novo coronary artery disease.

BACKGROUND: The use of biodegradable polymer drug-eluting stents (BP-DES) has been proven to minimize restenosis and stent thrombosis. The current post-marketing monitoring was observed at the 5-year clinical outcomes of individuals who had been treated with FlexyRap® DES in the real world. AIM: To assess the safety and effectiveness of FlexyRap® DES at the 5-year follow-up in real-world settings. METHODS: Findings from a retrospective, multi-center, observational, post-market clinical follow-up study of patients treated with FlexyRap® DES for de novo coronary artery disease (CAD) were reported. During the 12-mo follow-up, the primary endpoint was target lesion failure, which was defined as the composite of cardiovascular death, target vessel myocardial infarction (TV-MI), and clinically driven target lesion revascularization. RESULTS: The data of 500 patients received with FlexyRap® DES was obtained at the completion of the surveillance timeline of 5-year. After the implantation of FlexyRap® DES, the device success rate was 100%. Adverse events that led to major bleeding, permanent disability, or death were not experienced in the patients. The major adverse cardiac event rate at 12-mo, 3-year, and 5-year follow-up was 1 (0.2%), 0 (0%), and 1 (0.2%) respectively with 0 (0%) cardiovascular death, 2 (0.4%) TV-MI, and 0 (0%) TLR compositely. Furthermore, late stent thrombosis was found in 2 (0.4%) patients at the follow-up of 12-mo, very late stent thrombosis was observed in 2 patients (0.4%) at 3-year follow-up. CONCLUSION: FlexyRap® DES was proved to be safe and efficacious in real-world patients with de novo CAD, indicating a lowered rate of cardiac events and stent thrombosis at 5-year follow-up.

QC_SANE: Robust Control in DRL Using Quantile Critic With Spiking Actor and Normalized Ensemble.

Recently introduced deep reinforcement learning (DRL) techniques in discrete-time have resulted in significant advances in online games, robotics, and so on. Inspired from recent developments, we have proposed an approach referred to as Quantile Critic with Spiking Actor and Normalized Ensemble (QC_SANE) for continuous control problems, which uses quantile loss to train critic and a spiking neural network (NN) to train an ensemble of actors. The NN does an internal normalization using a scaled exponential linear unit (SELU) activation function and ensures robustness. The empirical study on multijoint dynamics with contact (MuJoCo)-based environments shows improved training and test results than the state-of-the-art approach: population coded spiking actor network (PopSAN).

A comprehensive review on variants of SARS-CoVs-2: Challenges, solutions and open issues.

SARS-CoV-2 is an infected disease caused by one of the variants of Coronavirus which emerged in December 2019. It is declared a pandemic by WHO in March 2020. COVID-19 outbreak has put the world on a halt and is a major threat to the public health system. It has shattered the world with its effects on different areas as the pandemic hit the world in a number of waves with different variants and mutations. Each variant and mutation have different transmission and infection rates in the human population. More than 609 million people have tested positive and more than 6.5 million people have died due to this disease as per 14th September 2022. Despite of numerous efforts, precautions and vaccination the infection has grown rapidly in the world. In this paper, we aim to give a holistic overview of COVID-19 its variants, game theory perspective, effects on the different social and economic areas, diagnostic advancements, treatment methods. A taxonomy is made for the proper insight of the work demonstrated in the paper. Finally, we discuss the open issues associated with COVID-19 in different fields and futuristic research trends in the area. The main aim of the paper is to provide comprehensive literature that covers all the areas and provide an expert understanding of the COVID-19 techniques and potentially be further utilized to combat the outbreak of COVID-19.

Machine Learning approach to Predict net radiation over crop surfaces from global solar radiation and canopy temperature data.

As the ground-based instruments for measuring net radiation are costly and need to be handled skillfully, the net radiation data at spatial and temporal scales over Indian subcontinent are scanty. Sometimes, it is necessary to use other meteorological parameters to estimate the value of net radiation, although the prediction may vary based on season, ground cover and estimation method. In this context, artificial intelligence can be used as a powerful tool for predicting the data considering past observed data. This paper proposes a novel method to predict the net radiation for five crop surfaces using global solar radiation and canopy temperature. This contribution includes the generation of real-time data for five crops grown in West Bengal state of India. After manual analysis and data preprocessing, data normalization has been done before applying machine learning approaches for training a robust model. We have presented the comparison in various machine learning algorithm such as ridge and spline regression, random forest, ensemble and deep neural networks. The result shows that the gradient boosting regression and ridge regression are outperforming other ML approaches. The estimated predictors enable to reduce the number of resources in terms of time, cost and manpower for proper net radiation estimation. Thus, the problem of predicting net radiation over various crop surfaces can be sorted out through ML algorithm.

Hybrid Technique for Cyber-Physical Security in Cloud-Based Smart Industries.

New technologies and trends in industries have opened up ways for distributed establishment of Cyber-Physical Systems (CPSs) for smart industries. CPSs are largely based upon Internet of Things (IoT) because of data storage on cloud servers which poses many constraints due to the heterogeneous nature of devices involved in communication. Among other challenges, security is the most daunting challenge that contributes, at least in part, to the impeded momentum of the CPS realization. Designers assume that CPSs are themselves protected as they cannot be accessed from external networks. However, these days, CPSs have combined parts of the cyber world and also the physical layer. Therefore, cyber security problems are large for commercial CPSs because the systems move with one another and conjointly with physical surroundings, i.e., Complex Industrial Applications (CIA). Therefore, in this paper, a novel data security algorithm Dynamic Hybrid Secured Encryption Technique (DHSE) is proposed based on the hybrid encryption scheme of Advanced Encryption Standard (AES), Identity-Based Encryption (IBE) and Attribute-Based Encryption (ABE). The proposed algorithm divides the data into three categories, i.e., less sensitive, mid-sensitive and high sensitive. The data is distributed by forming the named-data packets (NDPs) via labelling the names. One can choose the number of rounds depending on the actual size of a key; it is necessary to perform a minimum of 10 rounds for 128-bit keys in DHSE. The average encryption time taken by AES (Advanced Encryption Standard), IBE (Identity-based encryption) and ABE (Attribute-Based Encryption) is 3.25 ms, 2.18 ms and 2.39 ms, respectively. Whereas the average time taken by the DHSE encryption algorithm is 2.07 ms which is very much less when compared to other algorithms. Similarly, the average decryption times taken by AES, IBE and ABE are 1.77 ms, 1.09 ms and 1.20 ms and the average times taken by the DHSE decryption algorithms are 1.07 ms, which is very much less when compared to other algorithms. The analysis shows that the framework is well designed and provides confidentiality of data with minimum encryption and decryption time. Therefore, the proposed approach is well suited for CPS-IoT.

Listening to bluetooth beacons for epidemic risk mitigation.

The ongoing COVID-19 pandemic let to efforts to develop and deploy digital contact tracing systems to expedite contact tracing and risk notification. Unfortunately, the success of these systems has been limited, partly owing to poor interoperability with manual contact tracing, low adoption rates, and a societally sensitive trade-off between utility and privacy. In this work, we introduce a new privacy-preserving and inclusive system for epidemic risk assessment and notification that aims to address these limitations. Rather than capturing pairwise encounters between user devices as done by existing systems, our system captures encounters between user devices and beacons placed in strategic locations where infection clusters may originate. Epidemiological simulations using an agent-based model demonstrate that, by utilizing location and environmental information and interoperating with manual contact tracing, our system can increase the accuracy of contact tracing actions and may help reduce epidemic spread already at low adoption.

Enhanced Network Intrusion Detection System.

A reasonably good network intrusion detection system generally requires a high detection rate and a low false alarm rate in order to predict anomalies more accurately. Older datasets cannot capture the schema of a set of modern attacks; therefore, modelling based on these datasets lacked sufficient generalizability. This paper operates on the UNSW-NB15 Dataset, which is currently one of the best representatives of modern attacks and suggests various models. We discuss various models and conclude our discussion with the model that performs the best using various kinds of evaluation metrics. Alongside modelling, a comprehensive data analysis on the features of the dataset itself using our understanding of correlation, variance, and similar factors for a wider picture is done for better modelling. Furthermore, hypothetical ponderings are discussed for potential network intrusion detection systems, including suggestions on prospective modelling and dataset generation as well.

Coronavirus Disease (Covid-19) Associated Mucormycosis (CAM): Case Report and Systematic Review of Literature.

Severe coronavirus disease (COVID-19) is currently managed with systemic glucocorticoids. Opportunistic fungal infections are of concern in such patients. While COVID-19 associated pulmonary aspergillosis is increasingly recognized, mucormycosis is rare. We describe a case of probable pulmonary mucormycosis in a 55-year-old man with diabetes, end-stage kidney disease, and COVID-19. The index case was diagnosed with pulmonary mucormycosis 21 days following admission for severe COVID-19. He received 5 g of liposomal amphotericin B and was discharged after 54 days from the hospital. We also performed a systematic review of the literature and identified seven additional cases of COVID-19 associated mucormycosis (CAM). Of the eight cases included in our review, diabetes mellitus was the most common risk factor. Three subjects had no risk factor other than glucocorticoids for COVID-19. Mucormycosis usually developed 10-14 days after hospitalization. All except the index case died. In two subjects, CAM was diagnosed postmortem. Mucormycosis is an uncommon but serious infection that complicates the course of severe COVID-19. Subjects with diabetes mellitus and multiple risk factors may be at a higher risk for developing mucormycosis. Concurrent glucocorticoid therapy probably heightens the risk of mucormycosis. A high index of suspicion and aggressive management is required to improve outcomes.

Smoking status and mortality outcomes following percutaneous coronary intervention.

OBJECTIVE: The aim of this study was to assess the impact of smoking on short (30-day) and intermediate (30-day to 6-month) mortality following percutaneous coronary intervention (PCI). BACKGROUND: The effect of smoking on mortality post-PCI is lacking in the modern PCI era. METHODS: This was a retrospective analysis of prospectively collected data comparing short- and intermediate-term mortality amongst smokers, ex-smokers and non-smokers. RESULTS: The study cohort consisted of 12,656 patients: never-smokers (n = 4288), ex-smokers (n = 4806) and current smokers (n = 3562). The mean age (±standard deviation) was 57 (±11) years in current smokers compared with 67 (±11) in ex-smokers and 67 (±12) in never-smokers; p < 0.0001. PCI was performed for acute coronary syndrome (ACS) in 84.1% of current smokers, 57% of ex-smokers and 62.9% in never-smokers; p < 0.0001. In a logistic regression model, the adjusted odds ratios (95% confidence intervals (CIs)) for 30-day mortality were 1.60 (1.10-2.32) in current smokers and 0.98 (0.70-1.38) in ex-smokers compared with never-smokers. In the Cox proportional hazard model, the adjusted hazard ratios (95% CI) for mortality between 30 days and 6 months were 1.03 (0.65-1.65) in current smokers and 1.19 (0.84-1.67) in ex-smokers compared with never-smokers. CONCLUSION: This large observational study of non-selected patients demonstrates that ex-smokers and never-smokers are of similar age at first presentation to PCI, and there is no short- or intermediate-term mortality difference between them following PCI. Current smokers undergo PCI at a younger age, more often for ACS, and have higher short-term mortality. These findings underscore the public message on the benefits of smoking cessation and the harmful effects of smoking.

An Explainable Intelligence Driven Query Prioritization Using Balanced Decision Tree Approach for Multi-Level Psychological Disorders Assessment.

Human emotions affect psychological health to a great level. Positive emotions relate to health improvement; whereas negative emotions may aggravate psychological disorders such as anxiety, stress, and depression. Although there exist several computational methods to predict psychological disorders, most of them provide a black-box view of uncertainty. This research involves developing a novel predictive model for multi class psychological risk recognition with an accurate explainable interface. Standard questionnaires are utilized as data set and a new approach called a Q-Prioritization is employed to drop insignificant questions from the data set. Moreover, a novel balanced decision tree method based on repetitive oversampling is applied for the training and testing of the model. Predictive nature along with its contributing factors are interpreted with three techniques such as permuted feature importance, contrastive explanation, and counterfactual method, which together form a reasoning engine. The prediction outcome generated an impressive performance with an aggregated accuracy of 98.25%. The mean precision, recall, and F-score metric recorded were 0.98, 0.977, and 0.979, respectively. Also, it was noted that without applying Q-Prioritization, the accuracy significantly drops to 90.25%. The error rate observed with our model was only 0.026. The proposed multi-level psychological disorder predictive model can successfully serve as an assistive deployment for medical experts in the effective treatment of mental health.

Calcaneal Osteomyelitis Treated with Antibiotic Mixed Calcium Sulphate Pellets. A Case Report.

Background: Calcaneal osteomyelitis is a complicated entity and much more challenging to treat. We present a case of calcaneal osteomyelitis treated with debridement, curettage and filling bone defect with antibiotic mixed calcium sulphate pellets. Case Report: A female patient presented with pain, swelling and purulent discharge from the left heel. Culture studies and magnetic resonance imaging findings were suggestive of osteomyelitis of the calcaneum. The patient was planned for definitive treatment, and remission was achieved with systemic antibiotics, debridement, curettage, and filling dead space with antibiotic mixed calcium sulphate pellets. Conclusion: Calcaneal osteomyelitis is very resistant to treatment and results in poor functional outcome. Management of calcaneal osteomyelitis with single-stage strategy using antibiotic mixed calcium sulphate effectively eradicates the disease to attain overall good functional outcome for the patient.

Diagnosis of hairy cell leukaemia by fine needle aspiration cytology of lymph node.

BACKGROUND: Hairy cell leukaemia (HCL) is a rare lymphoproliferative disorder of B cell origin, and uncommonly it affects the lymph node. Fine needle aspiration cytology (FNAC) of lymph node of HCL has rarely been described. CASE DESCRIPTION AND DIAGNOSIS: A 41-year-old man presented with pallor, fever, tachycardia, generalized lymphadenopathy, and massive splenomegaly. The FNAC of the cervical lymph node was done. The smears showed many atypical lymphocytes with a plasmacytoid appearance. There were many large cells with round to reniform shaped nuclei having with hair-like cytoplasmic processes. Flow cytometry (FCM) revealed a clonal B cell population with light chain restriction and positive CD20, CD79b, CD22, CD11c, CD25, CD103, CD123, and CD200 markers. CONCLUSION: The characteristic cytological features such as atypical lymphoid cells, large cells with hairy projections along with FCM findings, are helpful in the diagnosis of HCL.