Role of ripasudil as an adjunct treatment in the management of pseudoexfoliative glaucoma.

Purpose: Ripasudil is a class of drug which alters the trabecular meshwork to increase the aqueous outflow and has been shown to be effective in pseudoexfoliative glaucoma (PXF G). This study aimed at assessing the efficacy and safety profile of ripasudil as an adjunct treatment in patients with PXF G at maximal tolerated antiglaucoma medications. Methods: In this prospective, interventional study, 40 patients with PXF G were enrolled between May 2021 and Jan 2022. Ripasudil 0.4% was started as an adjunctive drug to the ongoing antiglaucoma medications. On follow-up visits at 1, 3, and 6 months, the visual acuity, intraocular pressure (IOP), anterior segment, and fundus findings were evaluated. The premedication and postmedication IOP values were compared by paired t-test, and a P-value <0.05 was considered statistically significant. Results: Average age at recruitment was 60.02 ± 8.74 years. Baseline premedication IOP was 25.375 ± 3.276 mmHg. IOP reduction at 6 months was found to be statistically significant in all patients, with the maximal response being 24.13%. Also, 87.5% (35/40) of patients reached target IOP or even lower IOP at the end of study. There was no statistically significant association between the PXF grade and IOP. However, the grade of inferior iridocorneal angle pigmentation was found to be higher in eyes with elevated IOP (P < 0.05). Only three patients developed conjunctival hyperemia as an adverse reaction, which was mild and transient. Conclusion: Ripasudil showed additional IOP-lowering effect with other antiglaucoma medications and exhibited no significant side effects.

The diagnosis of ASD using multiple machine learning techniques.

Autism Spectrum Disorder (ASD) is a highly heterogeneous set of neurodevelopmental disorders with the global prevalence estimates of 2.20%, according to DSM5 criteria. With the advancements of technology and availability of huge amount of data, assistive tools for diagnosis of ASD are being developed using machine learning techniques. The present study examines the possibility of automating the Autism diagnostic tool using various machine learning techniques on a dataset of 701 samples that contains 10 fields from AQ-10-Adult and 10 from individual characteristics. It takes two scenarios into consideration. First one is ideal case, where there are no missing values in the test cases. In this case Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest (RF) classifiers are trained and tested on the pre-processed dataset. To reduce computational complexity Recursive Feature Elimination (RFE) based feature selection algorithm is applied. To deal with the real-world data, in the second case missing values are introduced in the test dataset for the fields' 'age', 'gender', 'jaundice', 'autism', 'used_app_before' and their three combinations. Support Vector Machine, Random Forest, Decision Tree and Logistic Regression based RFE algorithm is introduced to handle this scenario. ANN, SVM and RF classifier based learning models are trained with all the cases. Twelve classification models were generated with RFE, out of which best performing models specific to missing value were evaluated using test cases and suggested for ASD Diagnosis.

Adverse Drug Reaction Monitoring in Multidrug-Resistant Tuberculosis Patients Receiving Bedaquiline and Delamanid-Based Regimen.

Background and objectives  Tuberculosis (TB) is an airborne contagious illness caused by Mycobacterium tuberculosis. Ineffective anti-TB medication prolongs and exasperates illness, promotes disease spread, increases the probability of developing resistance to treatment, and increases death rate. Bedaquiline (BDQ) and Delamanid (DLM) were conditionally made available in the treatment of multidrug-resistant TB (MDR TB). In drug-resistant TB patients, adverse drug reactions (ADR) management is essential to improve medication compliance. In addition, we performed this study since there are very few studies published on the analysis of ADR monitoring of BDQ and DLM-based regimen. This study was performed to study the spectrum of ADR in drug-resistant TB patients receiving BDQ and DLM-based regimen. Methodology The study was conducted over a period of 26 months, in a hospital's departments of pharmacology and pulmonary medicine. Pre-extensively drug-resistant (Pre-XDR) and XDR TB were established on the basis of cartridge-based nucleic acid amplification (CB-NAAT), line probe assay (LPA), drug susceptibility testing (DST), and bacteriological culture. Patients were prescribed with appropriate medicines at the initial visit and any adverse reactions to medication were assessed in the subsequent visit. The statistical analysis was done using frequency distribution procedure, chi-square test of independence. The significance level was set at p<0.05. Results It was revealed that there were as many as 24 types of ADRs manifested in different patients. The most frequent ADR was QTcF (corrected heart rate) prolongation. Conclusion The maximum number of patients had some form of ADR and the percentage was slightly higher in the BDQ group than in the DLM group.

A soft computing tool for species classification and prediction of glucomannan content in Amorphophallus genus.

The proposed work aims at designing a classification system for automatic identification of A. muelleri species, grown as a potential cash crop in many Asian countries, from the DNA fingerprints of Amorphophallus genus. Four sets of 48 DNA fingerprints belonging to 37 species of the Amorphophallus genus, developed with the help of four different primers are considered for the experiment, with an objective to identify only the fingerprints of the species of interest. A second experimental setup deals with the automatic classification of species containing high amounts of glucomannan from the same set of DNA fingerprints of the Amorphophallus genus. For each set of 48 DNA fingerprints generated with a specific primer, the DNA fingerprints are preprocessed to extract a 42 dimensional feature vector which is used to generate a k-Nearest Neighbor based classifier based on the Leave One Out Cross Validation protocol. Final classification based on outputs from individual classifiers constructed with respect to the four different primers is performed according to a n-star consensus strategy. The n-star consensus predicts species A. muelleri with cent per cent accuracy while it predicts species containing glucomannan with a more modest accuracy of 81.25%.

Pricing and availability of some essential child specific medicines in Odisha.

OBJECTIVES: Continuous availability of affordable medicines in appropriate formulations is essential to reduce morbidity and mortality in children. Odisha an eastern Indian state records very high mortality of children. The study aims at documenting the availability and prices paid for purchasing essential child-specific medicines. MATERIALS AND METHODS: The survey of 34 essential medicines was conducted in six randomly selected districts of Odisha. Data were collected from medicine outlets of the public, private, and other sector (Nongovernmental Organization [NGO]/mission sectors) of six randomly selected districts, using WHO/Health Action International medicine price collection methodology. For each medicine surveyed, data were collected on the highest and lowest-priced formulations available in each facility. RESULTS: Both public sector and other sector health facilities procure only one brand of medicines, mean percentage availability of medicines being 17% and 21.8%, respectively. In the private sector, the mean percentage availability of the high and lowest-priced medicines for a particular drug product was 10.8% and 38.5%, respectively. The public sector procurement price is 48% lower than international reference prices. In the private sector, high-priced, and low-priced products are sold at 1.83 and 1.46 times the international reference price, respectively. Substantial price variation was observed for some medicines across individual outlets. Medicines were found to cost 2.08 times their international reference price in NGO/mission sector facilities. CONCLUSIONS: The availability of children's medicines in public sector facilities of Odisha state is poor. Medicines for children cost relatively high in both private and NGO sectors compared to the international reference price. The availability medicines should be improved on an urgent basis to improve access of medicines for children of Odisha.