Caffey Disease in Infancy: A diagnostic dilemma for primary care physicians

Caffey disease is a rare and self-limiting condition characterised by cortical hyperostosis with inflammation of adjacent fascia and muscles. It usually presents in infancy and clinical features include hyperirritability, acute inflammation with swelling of overlying soft tissues and subperiosteal new bone formation. Awareness of the existence of this rare condition and its typical clinical and radiological profile will avoid unnecessary investigations and treatment and help the physician to explain its good prognosis to parents of affected children. We report a three-month-old male infant who presented to the Outpatient Paediatrics Department at Moti Lal Nehru Medical College, Allahabad, India, in 2018 with a right shoulder mass, decreased upper limb movements and irritability. The patient was treated with ibuprofen and paracetamol. Irritability and limitation of movement improved over a treatment period of two weeks.Keywords: Caffey Disease; Infant; Prostaglandin E1; Thrombocytosis; Case Report; India

Sonographic fetal biometry charts for a Pakistani cohort.

This study aimed to develop growth centiles at different gestational weeks for fetal biparietal diameter, abdominal circumference, femur length and head circumference in a Pakistani cohort. Data were collected at a tertiary referral hospital from pregnant women at gestational ages 13-40 weeks referred for obstetric ultrasound as a part of routine antenatal care. A total of 1599 fetal sonographic biometric measurements were collected after screening for the inclusion criteria. For each measurement, separate regression models were derived to estimate the mean, standard deviation and reference percentiles at each week of gestational age for this cohort. The best fitting model for each variable was selected. These charts will help radiologists and clinicians in predicting dates of delivery, assessing fetal growth and identifying intrauterine fetal insufficiency in the Pakistani population

Effects of zinc oxide nanoparticles (ZnO NPs) and some plant pathogens on the growth and nodulation of lentil (Lens culinaris Medik.)

Effects of ZnO nanoparticles (NPs) were studied on lentil plants inoculated with Alternaria alternata, Fusarium oxysporum f. sp. lentis, Xanthomonas axonopodis pv. phaseoli, Pseudomonas syringae pv. syringae and Meloidogyne incognita. Plant growth, chlorophyll, carotenoid contents, nitrate reductase (NR) activity and nodulation of lentil both in the presence and absence of Rhizobium sp. were examined in a pot test. Inoculation of plants with A. alternata / F. oxysporum f. sp. lentis / X. axonopodis pv. phaseoli / P. syringae pv. syringae or M. incognita caused a significant reduction in plant growth, number of pods per plant, chlorophyll, carotenoids and NR activity over uninoculated control. Inoculation of plants with Rhizobium sp. with or without pathogen increased plant growth and number of pods per plant, chlorophyll, carotenoids and NR activity. When plants were grown without Rhizobium, a foliar spray of plants with 10 ml solution of 0.1 mg ml–1of ZnO NPs per plant caused a significant increase in plant growth and number of pods, chlorophyll, carotenoid contents and NR activity in both inoculated and uninoculated plants. Spray of ZnO NPs to plants inoculated with Rhizobium sp. caused non significant increase in plant growth, number of pods per plant, chlorophyll, carotenoid contents and NR activity when plants were either uninoculated or inoculated with pathogens. Numbers of nodules per root system were high in plants treated with Rhizobium sp. but foliar spray of ZnO NPs had adverse effect on nodulation. Inoculation of plants with test pathogens also reduced nodulation. Spray of ZnO NPs to plants reduced galling, nematode multiplication, wilt, blight and leaf spot disease severity indices

Global variation of COVID-19 mortality rates in the initial phase

Objectives: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused devastation in over 200 countries. Italy, Spain, and the United States (US) were most severely affected by the first wave of the pandemic. The reasons why some countries were more strongly affected than others remain unknown. We identified the most-affected and less-affected countries and states and explored environmental, host, and infrastructure risk factors that may explain differences in the SARS-CoV-2 mortality burden. Methods: We identified the top 10 countries/US states with the highest deaths per population until May 2020. For each of these 10 case countries/states, we identified 6 control countries/ states with a similar population size and at least 3 times fewer deaths per population. We extracted data for 30 risk factors from publicly available, trusted sources. We compared case and control countries/states using the non-parametric Wilcoxon rank-sum test, and conducted a secondary cluster analysis to explore the relationship between the number of cases per population and the number of deaths per population using a scalable EM (expectation–maximization) clustering algorithm. Results: Statistically significant differences were found in 16 of 30 investigated risk factors, the most important of which were temperature, neonatal and under-5 mortality rates, the percentage of under-5 deaths due to acute respiratory infections (ARIs) and diarrhea, and tuberculosis incidence (p < 0.05) Conclusion: Countries with a higher burden of baseline pediatric mortality rates, higher pediatric mortality from preventable diseases like diarrhea and ARI, and higher tuberculosis incidence had lower rates of coronavirus disease 2019-associated mortality, supporting the hygiene hypothesis

Analysis of L-citrulline and L-arginine in Ficus deltoidea leaf extracts by reverse phase high performance liquid chromatography

Ficus deltoidea (FD) is one of the native plants widely distributed in several countries in Southeast Asia. Previous studies have shown that FD leaf possess antinociceptive, wound healing and antioxidant properties. These beneficial effects have been attributed to the presence of primary and secondary metabolites such as polyphenols, amino acids and flavonoids. Objective: The aim was to develop a reverse phase high-performance liquid chromatography method with ultraviolet detection that involves precolumn derivatisation with O-phthaladehyde for simultaneous analysis of two amino acids L-citrulline and L-arginine in FD leaf extracts. Materials and Methods: An isocratic elution program consisting of methanol: acetonitrile: Water at 45:45:10 v/v (solvent A) and 0.1 M phosphate buffer pH 7.5 (solvent B) at A: B v/v ratio of 80:20 on Zorbax Eclipse C18 SB-Aq column (250 × 4.6 mm, 5 μm) were used. The flow rate was set at 1 ml/min and detection was carried out at 338 nm with 30 min separation time. Results: Good linearity for L-citrulline and L-arginine was obtained in the range 0.1-1000 μg/ml at R2 ≥ 0.998. The limit of detection and limit of quantification values for both L-citrulline and L-arginine were 1 and 5 μg/ml, respectively. The average of recoveries was in the range 94.94-101.95%, with relative standard deviation (%RSD) less than 3%. Intra- and inter-day precision was in the range 96.36-102.43% with RSD less than 2%. Conclusion: All validation parameters of the developed method indicate the method is reliable and efficient for simultaneous determination of L-citrulline and L-arginine for routine analysis of FD

A systematic review of physiological signals based driver drowsiness detection systems.

Driving a vehicle is a complex, multidimensional, and potentially risky activity demanding full mobilization and utilization of physiological and cognitive abilities. Drowsiness, often caused by stress, fatigue, and illness declines cognitive capabilities that affect drivers' capability and cause many accidents. Drowsiness-related road accidents are associated with trauma, physical injuries, and fatalities, and often accompany economic loss. Drowsy-related crashes are most common in young people and night shift workers. Real-time and accurate driver drowsiness detection is necessary to bring down the drowsy driving accident rate. Many researchers endeavored for systems to detect drowsiness using different features related to vehicles, and drivers' behavior, as well as, physiological measures. Keeping in view the rising trend in the use of physiological measures, this study presents a comprehensive and systematic review of the recent techniques to detect driver drowsiness using physiological signals. Different sensors augmented with machine learning are utilized which subsequently yield better results. These techniques are analyzed with respect to several aspects such as data collection sensor, environment consideration like controlled or dynamic, experimental set up like real traffic or driving simulators, etc. Similarly, by investigating the type of sensors involved in experiments, this study discusses the advantages and disadvantages of existing studies and points out the research gaps. Perceptions and conceptions are made to provide future research directions for drowsiness detection techniques based on physiological signals. [Abstract copyright: © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.