Study protocol for a randomized control trial investigating the effectiveness of a multifaceted mHealth approach on adherence to antihypertensive treatment among patients in Pakistan.

Background & Objectives: Poor medication adherence is an essential contributor to Pakistan's high prevalence of uncontrolled hypertension. This study will be aimed to assess the efficacy of a one-of-a-kind developed intervention in improving medication adherence and treatment outcomes in hypertension patients.

Methods: Twleve months duration long randomized controlled trial from January to December 2021 will be carried out at Shaikh Zayed Medical Complex (SZMC), Lahore.

Bias in mobility datasets drives divergence in modeled outbreak dynamics.

Background: Digital data sources such as mobile phone call detail records (CDRs) are increasingly being used to estimate population mobility fluxes and to predict the spatiotemporal dynamics of infectious disease outbreaks. Differences in mobile phone operators' geographic coverage, however, may result in biased mobility estimates.

Methods: We leverage a unique dataset consisting of CDRs from three mobile phone operators in Bangladesh and digital trace data from Meta's Data for Good program to compare mobility patterns across these sources.

Outcome of Double Layer UrethroplastyWithout Flap in Mid and Distal Penile Hypospadias.

Hypospadias is the absence of external urethral meatus in the tip of the glans but present in the ventral surface of the penis. Hypospadias surgery is challenging and changing. Many modifications have been done to reduce the complications.

The Role of Laparoscopy in Managing Impalpable Undescended Testes in a Tertiary Hospital.

One of the most prevalent congenital defects in boys is undescended testes. Ultrasound is commonly employed to locate the testis in cases of impalpable testes, which frequently yields false-negative results. Laparoscopy has gained widespread acceptance recently for managing impalpable testes.

Parameter-efficient fine-tuning of large language models using semantic knowledge tuning.

Large Language Models (LLMs) are gaining significant popularity in recent years for specialized tasks using prompts due to their low computational cost. Standard methods like prefix tuning utilize special, modifiable tokens that lack semantic meaning and require extensive training for best performance, often falling short. In this context, we propose a novel method called Semantic Knowledge Tuning (SK-Tuning) for prompt and prefix tuning that employs meaningful words instead of random tokens.