Identification of potential AChE inhibitors through combined machine-learning and structure-based design approaches-Supplementary data

Alzheimer’s disease (AD) is an irreversible, progressive neurodegenerative disease characterised by dementia.The depletion of acetylcholine (ACh) is involved the synaptic cleft is responsible for dementia due to neuronal loss. The acetylcholinesterase (AChE) enzyme isinvolved in the hydrolytic degradation of ACh and its inhibition is therapeutically beneficial for the treatment in memory loss.The use of machine learning (ML) for the identification of enzyme inhibitors has recently become popular. It identifies important patterns in the reported inhibitors to predict the new molecules. Hence, in this study, a set of support vector classifier-based ML models were developed,validated and employed to predict AChE inhibitors. Further, 247 predicted compounds obtained through PAINS and molecular property filters were docked on the AChE enzyme. The docking study identified compounds AAM132011183, ART21232619 and LMG16204648 as AChE inhibitors with suitable ADME properties. The selected compounds produced stable interactions with enzymes in molecular dynamics studies. The novel inhibitors obtained from the study may be proposed as active leads for AChE inhibition.

Identification of potential AChE inhibitors through combined machine-learning and structure-based design approaches-Supplementary data

Alzheimer’s disease (AD) is an irreversible, progressive neurodegenerative disease characterised by dementia.The depletion of acetylcholine (ACh) is involved the synaptic cleft is responsible for dementia due to neuronal loss. The acetylcholinesterase (AChE) enzyme isinvolved in the hydrolytic degradation of ACh and its inhibition is therapeutically beneficial for the treatment in memory loss.The use of machine learning (ML) for the identification of enzyme inhibitors has recently become popular. It identifies important patterns in the reported inhibitors to predict the new molecules. Hence, in this study, a set of support vector classifier-based ML models were developed,validated and employed to predict AChE inhibitors. Further, 247 predicted compounds obtained through PAINS and molecular property filters were docked on the AChE enzyme. The docking study identified compounds AAM132011183, ART21232619 and LMG16204648 as AChE inhibitors with suitable ADME properties. The selected compounds produced stable interactions with enzymes in molecular dynamics studies. The novel inhibitors obtained from the study may be proposed as active leads for AChE inhibition.

Identification of potential AChE inhibitors through combined machine-learning and structure-based design approaches

Alzheimer’s disease (AD) is an irreversible, progressive neurodegenerative disease characterised by dementia.The depletion of acetylcholine (ACh) is involved the synaptic cleft is responsible for dementia due to neuronal loss. The acetylcholinesterase (AChE) enzyme isinvolved in the hydrolytic degradation of ACh and its inhibition is therapeutically beneficial for the treatment in memory loss.The use of machine learning (ML) for the identification of enzyme inhibitors has recently become popular. It identifies important patterns in the reported inhibitors to predict the new molecules. Hence, in this study, a set of support vector classifier-based ML models were developed,validated and employed to predict AChE inhibitors. Further, 247 predicted compounds obtained through PAINS and molecular property filters were docked on the AChE enzyme. The docking study identified compounds AAM132011183, ART21232619 and LMG16204648 as AChE inhibitors with suitable ADME properties. The selected compounds produced stable interactions with enzymes in molecular dynamics studies. The novel inhibitors obtained from the study may be proposed as active leads for AChE inhibition.

Evaluation of Autologous Platelet Rich Plasma Injection in the Treatment of Rotator Cuff Tendinopathy

Background: Rotator cuff tendinopathy (RCT) is a significantsource of disability and loss of work. As commonly usedsubacromial corticosteroid injection for treatment of chronicrotator cuff tendinopathy has adverse effects especially inelderly people, new treatment options such as Platelet-RichPlasma (PRP) can be considered for managing of thispathology. The aim of the present study was conducted toevaluate the effectiveness of autologous platelet rich plasmainjection in the treatment of rotator cuff tendinopathy.Materials and Methods: The present study was conductedamong adults of age 30-70 years over the period of 1 year fromFeb 2018 to Jan 2019. The primary outcome measure for allparticipants was a score on a 0–10 visual analog scale (VAS)assessing current resting pain at baseline and at 8, 12, and 52weeks. Demographics and information about duration of RCTpain and prior therapies for RCT were collected. Eachparticipant underwent a single injection of PRP. In-personassessment occurred at 2, 8, and 12 weeks and by phone at52 weeks. Statistical analysis was done using SPSS 21software. P values less than .05 were considered statisticallysignificant for main and interaction effects.Results: In the present study total sample size was 46 inwhich 32 were males and 14 were females. VAS score wasevaluated for the treatment of Rotator cuff Tendinopathy atbaseline, 8 week, 12 week, 52 weeks after the injection ofautologous platelet rich plasma. The result shows that VASscore was less after 8 weeks and after 12 weeks and 52 weeksit was almost same.Conclusion: Our study concluded that pain was less in thepatients of Rotator cuff Tendinopathy after the injection ofautologous platelet rich plasma.

First Report of Agrobacterium rhizogenes-induced Hairy Root Formation in Selaginella bryopteris: a Pteridophyte Recalcitrant to Genetic Transformation

Abstract we report A. rhizogenes-induced hairy root formation in S. bryopteris, a medicinally and commercially important plant. A. rhizogenes strain LBA1334 co-cultivated with explants (root, rhizophore, stem portion near the root, and stem with intact fronds) for 24 and 48 h after transformation for induction of hairy roots. The induction of hairy root was observed after 6 days of infection in case of 48 h co-cultivation only. PCR with rolA and virC gene specific primers confirmed the induced hairy roots were due to Ri T-DNA integration and not due to contaminating A. rhizogenes. The root network as explants showed the maximum transformation efficiency. We tested different media like MS, SHFR (Stage Hog Fern Root) and KNOP's during transformation for hairy root induction. The SHFR based media showed good response in transformation as well as propagation. Further, transformation efficiency was enhanced by addition of TDZ (2 mg/L) and Bevistin (0.1%) in SHFR media. The present work would be helpful in hairy roots-based in vitro production of secondary metabolites and on aspect of functional genomics of S. bryopteris.

Comparison of clinical outcomes of sitagliptin+metformin combination and glimepiride in the management of uncomplicated type 2 diabetics

Background: To evaluate the comparison of clinical outcomes of sitagliptin +metformin and glimepiride in uncomplicated Type-2 diabetics.Methods: This one year (July 2016 to August 2017) prospective, open label, observational clinical cohort study was carried out on type-2 diabetics. In this study 299 Type-2 diabetics patients were enrolled and were randomly allocated to two groups viz Group A and Group B. Group A received sitaglitin+metformin (50+500) mg/day and Group B received glimepiride 1mg/day respectively. The follow up started after 10 days of stabilization of the patient and data recorded on 10th day was considered Zero month data and follow up continued up to Six month in each group. Comparison of FPG, PPG and HbA1c was evaluated between zero and six months within group and at six month between groups. Adverse events were recorded and summarized by treatment group.Results: At the end of six months follow up the patients of Group A who received sitaglitin+metformin (50+500) mg/day had greater reduction in FPG, PPG and HbA1c (all P<0.001) was recorded when compared between zero and six month within group. A significant reduction in FPG, PPG and HbA1c (all P<0.01) also recorded in Group B who received glimepiride 1mg/day when compared between zero and six months within group. A statically significant difference (all P<0.05) was recorded at six months between group. The adverse events like hypoglycemic episodes, gastrointestinal adverse events etc were greater in Group B than Group A. Changes in weight also noted in both Groups. Weight loss in Group A and weight gain in Group B was recorded.Conclusions: The present study suggests that a significant difference may be existing in the clinical outcome interm of glycemia control and adverse events between sitagliptin+metformin combination and glimepiride in type-2 diabetic patients.