Double-Blind Parallel Treatment Randomized Controlled Trial of Prebiotics' Efficacy for Children Experiencing Severe Acute Malnutrition in Southern Punjab, Pakistan

The prevalence of malnutrition among children under five is alarmingly high in Pakistan. However, there are ready-to-use therapeutic foods (RUTF) available which may be used to treat children with severe acute malnutrition (SAM). This study aims to assess the efficacy of prebiotics as a synergistic additive to RUTF to enhance blood parameters and anthropometric measurements in children with uncomplicated SAM living in Southern Punjab, Pakistan. A double-blind parallel treatment randomized controlled trial was conducted on 204 children aged 6-59 months. Participants were randomized and allocated to the placebo (n = 102) or experimental arms (n = 102) in a 1:1 ratio. One group of children was provided with RUTF and 4 g prebiotics, while the other group was given RUTF and starch as a placebo. Participants recruited for both arms were given treatment for eight weeks, and then their biochemical and anthropometric outcomes were evaluated. A substantial difference between the mean weight, mid-upper-arm circumference, haemoglobin, haematocrit, platelet count, mean corpuscular volume, mean corpuscular haemoglobin, and albumin was revealed. During the two-month follow-up phase of the trial, children who were enrolled in the treatment group gained about 20% of their initial weight (pre-study mean weight = 5.44 ± 1.35 kg; post-study mean weight = 6.53 ± 1.45 kg). The analysis showed a significant difference (p < 0.005) between the control and treatment groups for MUAC and complete blood counts. Conclusively, supplementation with RUTF and prebiotics has proven to be an efficient, effective, and safe therapy for children suffering from SAM to improve their growth and development indicators and reduce the dangers of malnutrition in comparison to RUTF alone.

COVID-19 Vaccination Acceptance in the Context of the Health Belief Model: Comparative Cross-Sectional Study in Punjab, Pakistan

One of the models that could be used to understand the adoption of vaccine uptake is the Health Belief Model (HBM). The aim of this study is to assess the role of HBM constructs and Perceived Health Status (PHS) on the vaccination status of individuals and to understand the role of socio-demographic variables on HBM scoring. A comparative cross-sectional telephone survey was conducted among 1325 vaccinated (60.0%) and non-vaccinated (40.0%) individuals aged 40 years and above in July 2021 in Punjab province, Pakistan. A higher level of education was the strongest predictor of positive HBM. All constructs of HBM, PHS and cues-to-action were significant predictors of COVID-19 vaccination uptake, with perceived benefits as the strongest predictor. In order to expand the vaccination coverage, double-pronged interventions utilizing both information and communication technology and human resources should be designed that address each barrier perceived by individuals and understandably communicate the benefits of COVID-19 vaccination to the broader population.

Automated Detection and Segmentation of Brain Metastases in Malignant Melanoma: Evaluation of a Dedicated Deep Learning Model

BACKGROUND AND PURPOSE

Malignant melanoma is an aggressive skin cancer in which brain metastases are common. Our aim was to establish and evaluate a deep learning model for fully automated detection and segmentation of brain metastases in patients with malignant melanoma using clinical routine MR imaging.

MATERIALS AND METHODS

Sixty-nine patients with melanoma with a total of 135 brain metastases at initial diagnosis and available multiparametric MR imaging datasets (T1-/T2-weighted, T1-weighted gadolinium contrast-enhanced, FLAIR) were included. A previously established deep learning model architecture (3D convolutional neural network; DeepMedic) simultaneously operating on the aforementioned MR images was trained on a cohort of 55 patients with 103 metastases using 5-fold cross-validation. The efficacy of the deep learning model was evaluated using an independent test set consisting of 14 patients with 32 metastases. Manual segmentations of metastases in a voxelwise manner (T1-weighted gadolinium contrast-enhanced imaging) performed by 2 radiologists in consensus served as the ground truth.

RESULTS

After training, the deep learning model detected 28 of 32 brain metastases (mean volume, 1.0 [SD, 2.4] cm³) in the test cohort correctly (sensitivity of 88%), while false-positive findings of 0.71 per scan were observed. Compared with the ground truth, automated segmentations achieved a median Dice similarity coefficient of 0.75.

CONCLUSIONS

Deep learning-based automated detection and segmentation of brain metastases in malignant melanoma yields high detection and segmentation accuracy with false-positive findings of <1 per scan.

Neurological complications in patients with Plasmodium vivax malaria from Karachi, Pakistan

BACKGROUND

Malaria remains an endemic disease in Pakistan with an estimated healthcare burden of 1.6 million cases annually, with Plasmodium vivax accounting for 67% of reported cases. P. vivax is the most common species causing malaria outside of Africa, with approximately 13.8 million reported cases worldwide.

METHOD

We report a series of P. vivax cases with cerebral involvement that presented at Aga Khan University Hospital, Karachi, Pakistan.

RESULTS

The majority of the patients presented with high-grade fever accompanied by projectile vomiting and abnormal behaviour, seizures, shock and unconsciousness. Seven of 801 patients with P. vivax monoinfection presented or developed cerebral complications. P. vivax infections were diagnosed based on peripheral smears and rapid diagnostic testing.

CONCLUSION

P. vivax infection can lead to severe complications, although not with the frequency of Plasmodium falciparum infection. Current cases highlight an increasing trend of cerebral complications caused by P. vivax.

Late prosthetic valve endocarditis caused by Staphylococcus hemolyticus

We describe a case of late PVE in a 78-year-old man that was caused by Stapylococcus hemolyticus and occurred 5 years after aortic valve replacement. This is the first reported case of PVE due to this organism.