Functional Outcomes in Failed Osteosynthesis of Proximal Femoral Fractures Managed by Total Hip Replacement Using the Modified Hardinge Approach: An Ambispective Cohort Study.

Introduction Proximal femoral fractures are a major cause of disability, particularly in aging populations, with an increasing incidence. Although osteosynthesis remains the first-line treatment, failures are common due to various complications. Total hip arthroplasty (THA) is the preferred salvage procedure in such cases, despite its technical challenges.

Evaluation of Bacterial Profiles and Antibiotic Susceptibility in Orthopedic Patients With Surgical Site Infections After Implant Surgery.

Introduction: Surgical site infections (SSIs) are a significant concern for surgeons and a leading cause of poor patient outcomes, increased morbidity, extended hospital stay, and higher costs. SSIs are of particular concern in orthopedic implant surgeries because biofilms on implants protect bacteria. This study aimed to evaluate the incidence rate of SSIs in orthopedic implant patients, identify associated risk factors, and analyze the types of bacteria causing these infections.

NucleoSeeker-precision filtering of RNA databases to curate high-quality datasets.

The structural prediction of biomolecules via computational methods complements the often involved wet-lab experiments. Unlike protein structure prediction, RNA structure prediction remains a significant challenge in bioinformatics, primarily due to the scarcity of annotated RNA structure data and its varying quality. Many methods have used this limited data to train deep learning models but redundancy, data leakage and bad data quality hampers their performance.

Freeze v/s air-dried alginate-pectin gel beads modified with sodium dodecyl sulphate for enhanced removal of copper ions.

This work presents a novel polymer-based adsorbent, Sodium Dodecyl Sulphate modified alginate-pectin gel beads (APS221) prepared via controlled freeze drying & air drying, for the removal of copper ions from the aqueous solution. This work also critically discusses the role played by various components and their concentrations in the success of APS221. Addition of pectin to alginate resulted into approximately 150 % increase in the metal removal performance of the adsorbent while addition of SDS into alginate-pectin complex enhanced the performance by 14 % approximately, taking the maximum adsorption capacity of final complex APS221 to 111.

Large-scale randomized experiments reveals that machine learning-based instruction helps people memorize more effectively.

We perform a large-scale randomized controlled trial to evaluate the potential of machine learning-based instruction sequencing to improve memorization while allowing the learners the freedom to choose their review times. After controlling for the length and frequency of study, we find that learners for whom a machine learning algorithm determines which questions to include in their study sessions remember the content over ~69% longer. We also find that the sequencing algorithm has an effect on users' engagement.

RecipeDB: a resource for exploring recipes.

Cooking is the act of turning nature into the culture, which has enabled the advent of the omnivorous human diet. The cultural wisdom of processing raw ingredients into delicious dishes is embodied in their cuisines. Recipes thus are the cultural capsules that encode elaborate cooking protocols for evoking sensory satiation as well as providing nourishment.

Recent advances in heavy metal removal by chitosan based adsorbents.

Chitosan, a low cost polymer, has been used in many studies for adsorption of heavy metal ions. This review covers the performance of all those adsorbents which were derived from chitosan for the adsorption of heavy metal ions in recent past. Further, the common chitosan modifications methods have been discussed in this paper among which crosslinking and grafting were found to be the most popular methods.

Enhancing human learning via spaced repetition optimization.

Spaced repetition is a technique for efficient memorization which uses repeated review of content following a schedule determined by a spaced repetition algorithm to improve long-term retention. However, current spaced repetition algorithms are simple rule-based heuristics with a few hard-coded parameters. Here, we introduce a flexible representation of spaced repetition using the framework of marked temporal point processes and then address the design of spaced repetition algorithms with provable guarantees as an optimal control problem for stochastic differential equations with jumps.