Pathogenic mutations in TULP1 responsible for retinitis pigmentosa identified in consanguineous familial cases.

PurposeTo identify pathogenic mutations responsible for autosomal recessive retinitis pigmentosa (arRP) in consanguineous familial cases.MethodsSeven large familial cases with multiple individuals diagnosed with retinitis pigmentosa were included in the study. Affected individuals in these families underwent ophthalmic examinations to document the symptoms and confirm the initial diagnosis. Blood samples were collected from all participating members, and genomic DNA was extracted. An exclusion analysis with microsatellite markers spanning the TULP1 locus on chromosome 6p was performed, and two-point logarithm of odds (LOD) scores were calculated. All coding exons along with the exon-intron boundaries of TULP1 were sequenced bidirectionally. We constructed a single nucleotide polymorphism (SNP) haplotype for the four familial cases harboring the K489R allele and estimated the likelihood of a founder effect.ResultsThe ophthalmic examinations of the affected individuals in these familial cases were suggestive of RP. Exclusion analyses confirmed linkage to chromosome 6p harboring TULP1 with positive two-point LOD scores. Subsequent Sanger sequencing identified the single base pair substitution in exon14, c.1466A>G (p.K489R), in four families. Additionally, we identified a two-base deletion in exon 4, c.286_287delGA (p.E96Gfs77*); a homozygous splice site variant in intron 14, c.1495+4A>C; and a novel missense variation in exon 15, c.1561C>T (p.P521S). All mutations segregated with the disease phenotype in the respective families and were absent in ethnically matched control chromosomes. Haplotype analysis suggested (p<10(-6)) that affected individuals inherited the causal mutation from a common ancestor.ConclusionsPathogenic mutations in TULP1 are responsible for the RP phenotype in seven familial cases with a common ancestral mutation responsible for the disease phenotype in four of the seven families

Seasonal and long term variations of surface ozone concentrations in Malaysian Borneo

Malaysian Borneo has a lower population density and is an area known for its lush rainforests. However, changes in pollutant profiles are expected due to increasing urbanisation and commercial-industrial activities. This study aims to determine the variation of surface {O3} concentration recorded at seven selected stations in Malaysian Borneo. Hourly surface {O3} data covering the period 2002 to 2013, obtained from the Malaysian Department of Environment (DOE), were analysed using statistical methods. The results show that the concentrations of {O3} recorded in Malaysian Borneo during the study period were below the maximum Malaysian Air Quality Standard of 100 ppbv. The hourly average and maximum {O3} concentrations of 31 and 92 ppbv reported at Bintulu (S3) respectively were the highest among the {O3} concentrations recorded at the sampling stations. Further investigation on {O3} precursors show that sampling sites located near to local petrochemical industrial activities, such as Bintulu (S3) and Miri (S4), have higher NO2/NO ratios (between 3.21 and 5.67) compared to other stations. The normalised {O3} values recorded at all stations were higher during the weekend compared to weekdays (unlike its precursors) which suggests the influence of {O3} titration by {NO} during weekdays. The results also show that there are distinct seasonal variations in {O3} across Borneo. High surface {O3} concentrations were usually observed between August and September at all stations with the exception of station {S7} on the east coast. Majority of the stations (except {S1} and S6) have recorded increasing averaged maximum concentrations of surface {O3} over the analysed years. Increasing trends of {NO2} and decreasing trends of {NO} influence the yearly averaged maximum of {O3} especially at S3. This study also shows that variations of meteorological factors such as wind speed and direction, humidity and temperature influence the concentration of surface O3

A 30-day follow-up study on the prevalence of SARS-COV-2 genetic markers in wastewater from the residence of COVID-19 patient and comparison with clinical positivity

Wastewater based epidemiology (WBE) is an important tool to fight against COVID-19 as it provides insights into the health status of the targeted population from a small single house to a large municipality in a cost-effective, rapid, and non-invasive way. The implementation of wastewater based surveillance (WBS) could reduce the burden on the public health system, management of pandemics, help to make informed decisions, and protect public health. In this study, a house with COVID-19 patients was targeted for monitoring the prevalence of SARS-CoV-2 genetic markers in wastewa-ter samples (WS) with clinical specimens (CS) for a period of 30 days. RT-qPCR technique was employed to target non-structural (ORF1ab) and structural-nucleocapsid (N) protein genes of SARS-CoV-2, according to a validated experimental protocol. Physiological, environmental, and biological parameters were also measured following the American Public Health Association (APHA) standard protocols. SARS-CoV-2 viral shedding in wastewater peaked when the highest number of COVID-19 cases were clinically diagnosed. Throughout the study period, 7450 to 23,000 gene copies/1000 mL were detected, where we identified 47 % (57/120) positive samples from WS and 35 % (128/360) from CS. When the COVID-19 patient number was the lowest (2), the highest CT value (39.4; i.e., lowest copy number) was identified from WS. On the other hand, when the COVID-19 patients were the highest (6), the lowest CT value (25.2 i.e., highest copy numbers) was obtained from WS. An advance signal of increased SARS-CoV-2 viral load from the COVID-19 patient was found in WS earlier than in the CS. Using customized primer sets in a traditional PCR approach, we confirmed that all SARS-CoV-2 variants identified in both CS and WS were Delta variants (B.1.617.2). To our knowledge, this is the first follow-up study to determine a temporal relationship be-tween COVID-19 patients and their discharge of SARS-CoV-2 RNA genetic markers in wastewater from a single house including all family members for clinical sampling from a developing country (Bangladesh), where a proper sewage system is lacking. The salient findings of the study indicate that monitoring the genetic markers of the SARS-CoV-2 virus in wastewater could identify COVID-19 cases, which reduces the burden on the public health system during COVID-19 pandemics.Peer reviewe

Characterisation of particle mass and number concentration on the east coast of the Malaysian Peninsula during the northeast monsoon

Particle mass concentrations (PM10, PM2.5 and PM1) and particle number concentration ((PNC); 0.27 μm ≤ Dp ≤ 34.00 μm) were measured in the tropical coastal environment of Bachok, Kelantan on the Malaysian Peninsula bordering the southern edge of the South China Sea. Statistical methods were applied on a three-month hourly data set (9th January to 24th March 2014) to study the influence of north-easterly winds on the patterns of particle mass and PNC size distributions. The 24-h concentrations of particle mass obtained in this study were below the standard values detailed by the Recommended Malaysian Air Quality Guideline (RMAQG), United States Environmental Protection Agency (US EPA) and European Union (EU) except for PM2.5, which recorded a 24-h average of 30 ± 18 μg m-3 and exceeded the World Health Organisation (WHO) threshold value (25 μg m-3). Principal component analysis (PCA) revealed that PNC with smaller diameter sizes (0.27-4.50 μm) showed a stronger influence, accounting for 57.6% of the variability in PNC data set. Concentrations of both particle mass and PNC increased steadily in the morning with a distinct peak observed at around 8.00 h, related to a combination of dispersion of accumulated particles overnight and local traffic. In addition to local anthropogenic, agricultural burning and forest fire activities, long-range transport also affects the study area. Hotspot and backward wind trajectory observations illustrated that the biomass burning episode (around February-March) significantly influenced PNC. Meteorological parameters influenced smaller size particles (i.e. PM1 and Dp (0.27-0.43 μm)) the most

Low Temperature Synthesis of Superparamagnetic Iron Oxide (Fe3O4) Nanoparticles and Their ROS Mediated Inhibition of Biofilm Formed by Food-Associated Bacteria

In the present study, a facile environmentally friendly approach was described to prepare monodisperse iron oxide (Fe3O4) nanoparticles (IONPs) by low temperature solution route. The synthesized nanoparticles were characterized using x-ray diffraction spectroscopy (XRD), Raman spectroscopy, field emission scanning electron microscopy (FESEM) measurements, Fourier-Transform Infrared Spectroscopy (FTIR), and Thermogravimetric analysis (TGA) analyses. XRD patterns revealed high crystalline quality of the nanoparticles. SEM micrographs showed the monodispersed IONPs with size ranging from 6 to 9 nm. Synthesized nanoparticles demonstrated MICs of 32, 64, and 128 μg/ml against Gram negative bacteria i.e., Serratia marcescens, Escherichia coli, and Pseudomonas aeruginosa, respectively, and 32 μg/ml against Gram positive bacteria Listeria monocytogenes. IOPNs at its respective sub-MICs demonstrated significant reduction of alginate and exopolysaccharide production and subsequently demonstrated broad-spectrum inhibition of biofilm ranging from 16 to 88% in the test bacteria. Biofilm reduction was also examined using SEM and Confocal Laser Scanning Microscopy (CLSM). Interaction of IONPs with bacterial cells generated ROS contributing to reduced biofilm formation. The present study for the first time report that these IONPs were effective in obliterating pre-formed biofilms. Thus, it is envisaged that these nanoparticles with broad-spectrum biofilm inhibitory property could be exploited in the food industry as well as in medical settings to curtail biofilm based infections and losses

Calibration model of a low-cost air quality sensor using an adaptive neuro-fuzzy inference system

Conventional air quality monitoring systems, such as gas analysers, are commonly used in many developed and developing countries to monitor air quality. However, these techniques have high costs associated with both installation and maintenance. One possible solution to complement these techniques is the application of low-cost air quality sensors (LAQSs), which have the potential to give higher spatial and temporal data of gas pollutants with high precision and accuracy. In this paper, we present DiracSense, a custom-made LAQS that monitors the gas pollutants ozone (O3), nitrogen dioxide (NO2), and carbon monoxide (CO). The aim of this study is to investigate its performance based on laboratory calibration and field experiments. Several model calibrations were developed to improve the accuracy and performance of the LAQS. Laboratory calibrations were carried out to determine the zero offset and sensitivities of each sensor. The results showed that the sensor performed with a highly linear correlation with the reference instrument with a response-time range from 0.5 to 1.7 min. The performance of several calibration models including a calibrated simple equation and supervised learning algorithms (adaptive neuro-fuzzy inference system or ANFIS and the multilayer feed-forward perceptron or MLP) were compared. The field calibration focused on O3 measurements due to the lack of a reference instrument for CO and NO2. Combinations of inputs were evaluated during the development of the supervised learning algorithm. The validation results demonstrated that the ANFIS model with four inputs (WE OX, AE OX, T, and NO2) had the lowest error in terms of statistical performance and the highest correlation coefficients with respect to the reference instrument (0.8 < r < 0.95). These results suggest that the ANFIS model is promising as a calibration tool since it has the capability to improve the accuracy and performance of the low-cost electrochemical sensor