EPIDEMIOLOGY OF OVINE GASTROINTESTINAL NEMATODES IN HYDERABAD DISTRICT, PAKISTAN

An epidemiological study on gastrointestinal nematodes of sheep was carried out in farms of small farmers in Hyderabad district from May 2004 to April 2005. Faecal egg counts, pasture larval counts and worm counts from permanent grazing animals were recorded for 12 months. H. contortus (24.6%) was found to be predominant of gastrointestinal nematode parasites, Trichostrongylus spp. (18.0%) was the next most prevalent species, others, including: O. circumcincta, S. papillosus, T. ovis, Oe. columbianum and Chabertia ovina were found in varying percentages. The highest faecal egg counts (FEC) were recorded in September, whereas the lower FEC were in February. Statistical analysis revealed that the FEC were significantly (P<0.01) affected by months (seasons). The peak of pasture infectivity was in August and declined to lower level in January. The mean worm burden counts were the highest in September and declined toward the minimum level in February in necropsized animals. The worm counts was influenced significantly (P<0.01) by FEC and pasture larval counts. The results of this study could be used to design a programme to minimize and control gastrointestinal nematode infections in sheep

Safety and efficacy of arimoclomol for inclusion body myositis: a multicentre, randomised, double-blind, placebo-controlled trial

BACKGROUND: Inclusion body myositis is the most common progressive muscle wasting disease in people older than 50 years, with no effective drug treatment. Arimoclomol is an oral co-inducer of the cellular heat shock response that was safe and well-tolerated in a pilot study of inclusion body myositis, reduced key pathological markers of inclusion body myositis in two in-vitro models representing degenerative and inflammatory components of this disease, and improved disease pathology and muscle function in mutant valosin-containing protein mice. In the current study, we aimed to assess the safety, tolerability, and efficacy of arimoclomol in people with inclusion body myositis. METHODS: This multicentre, randomised, double-blind, placebo-controlled study enrolled adults in specialist neuromuscular centres in the USA (11 centres) and UK (one centre). Eligible participants had a diagnosis of inclusion body myositis fulfilling the European Neuromuscular Centre research diagnostic criteria 2011. Participants were randomised (1:1) to receive either oral arimoclomol 400 mg or matching placebo three times daily (1200 mg/day) for 20 months. The randomisation sequence was computer generated centrally using a permuted block algorithm with randomisation numbers masked to participants and trial staff, including those assessing outcomes. The primary endpoint was the change from baseline to month 20 in the Inclusion Body Myositis Functional Rating Scale (IBMFRS) total score, assessed in all randomly assigned participants, except for those who were randomised in error and did not receive any study medication, and those who did not meet inclusion criteria. Safety analyses included all randomly assigned participants who received at least one dose of study medication. This trial is registered with ClinicalTrials.gov, number NCT02753530, and is completed. FINDINGS: Between Aug 16, 2017 and May 22, 2019, 152 participants with inclusion body myositis were randomly assigned to arimoclomol (n=74) or placebo (n=78). One participant was randomised in error (to arimoclomol) but not treated, and another (assigned to placebo) did not meet inclusion criteria. 150 participants (114 [76%] male and 36 [24%] female) were included in the efficacy analyses, 73 in the arimoclomol group and 77 in the placebo group. 126 completed the trial on treatment (56 [77%] and 70 [90%], respectively) and the most common reason for treatment discontinuation was adverse events. At month 20, mean IBMFRS change from baseline was not statistically significantly different between arimoclomol and placebo (-3·26, 95% CI -4·15 to -2·36 in the arimoclomol group vs -2·26, -3·11 to -1·41 in the placebo group; mean difference -0·99 [95% CI -2·23 to 0·24]; p=0·12). Adverse events leading to discontinuation occurred in 13 (18%) of 73 participants in the arimoclomol group and four (5%) of 78 participants in the placebo group. Serious adverse events occurred in 11 (15%) participants in the arimoclomol group and 18 (23%) in the placebo group. Elevated transaminases three times or more of the upper limit of normal occurred in five (7%) participants in the arimoclomol group and one (1%) in the placebo group. Tubulointerstitial nephritis was observed in one (1%) participant in the arimoclomol group and none in the placebo group. INTERPRETATION: Arimoclomol did not improve efficacy outcomes, relative to placebo, but had an acceptable safety profile in individuals with inclusion body myositis. This is one of the largest trials done in people with inclusion body myositis, providing data on disease progression that might be used for subsequent clinical trial design. FUNDING: US Food and Drug Administration Office of Orphan Products Development and Orphazyme

OVICIDAL AND LARVICIDAL PROPERTIES OF ADHATODA VASICA (L.) EXTRACTS AGAINST GASTROINTESTINAL NEMATODES OF SHEEP IN VITRO

The main objective of this study was to evaluate the anthelmintic activity of Adhatoda vasica (Acanthaceae) in vitro against the gastrointestinal nematodes of sheep. The aqueous and ethanolic extracts of Adhatoda vasica aerial parts were evaluated by egg hatching and larval development assays. The aqueous and ethanolic extracts at 25-50 mg/ml concentrations exhibited ovicidal and larvicidal (p<0.05) activity against gastrointestinal nematodes. The plant extracts showed dose-dependent inhibition (P<0.05). The ethanolic extract at the concentration of 50.0 mg/ml was more effective in inhibiting egg hatching and larval development of gastrointestinal nematodes. The effective dose (ED50) of aqueous and ethanolic extracts were determined graphically from linear regression equation with probit scale, y = 5. The results of this study suggested that Adhatoda vasica extracts may be useful in the control of gastrointestinal nematodes of sheep

Spatiotemporal Variability Assessment of Trace Metals Based on Subsurface Water Quality Impact Integrated with Artificial Intelligence-Based Modeling

Increasing anthropogenic emissions due to rapid industrialization have triggered environmental pollution and pose a threat to the well-being of the ecosystem. In this study, the first scenario involved the spatio-temporal assessment of topsoil contamination with trace metals in the Dammam region, and samples were taken from 2 zones: the industrial (ID), and the agricultural (AG) area. For this purpose, more than 130 spatially distributed samples of topsoil were collected from residential, industrial, and agricultural areas. Inductively coupled plasma—optical emission spectroscopy (ICP-OES)—was used to analyze the samples for various trace metals. The second scenario involved the creation of different artificial intelligence (AI) models, namely an artificial neural network (ANN) and a support vector regression (SVR), for the estimation of zinc (Zn), copper (Cu), chromium (Cr), and lead (Pb) using feature-based input selection. The experimental outcomes depicted that the average concentration levels of HMs were as follows: Chromium (Cr) (31.79 ± 37.9 mg/kg), Copper (Cu) (6.76 ± 12.54 mg/kg), Lead (Pb) (6.34 ± 14.55 mg/kg), and Zinc (Zn) (23.44 ± 84.43 mg/kg). The modelling accuracy, based on different evaluation criteria, showed that agricultural and industrial stations showed performance merit with goodness-of-fit ranges of 51–91% and 80–99%, respectively. This study concludes that AI models could be successfully applied for the rapid estimation of soil trace metals and related decision-making

Spatiotemporal Variability Assessment of Trace Metals Based on Subsurface Water Quality Impact Integrated with Artificial Intelligence-Based Modeling

Increasing anthropogenic emissions due to rapid industrialization have triggered environmental pollution and pose a threat to the well-being of the ecosystem. In this study, the first scenario involved the spatio-temporal assessment of topsoil contamination with trace metals in the Dammam region, and samples were taken from 2 zones: the industrial (ID), and the agricultural (AG) area. For this purpose, more than 130 spatially distributed samples of topsoil were collected from residential, industrial, and agricultural areas. Inductively coupled plasma&mdash;optical emission spectroscopy (ICP-OES)&mdash;was used to analyze the samples for various trace metals. The second scenario involved the creation of different artificial intelligence (AI) models, namely an artificial neural network (ANN) and a support vector regression (SVR), for the estimation of zinc (Zn), copper (Cu), chromium (Cr), and lead (Pb) using feature-based input selection. The experimental outcomes depicted that the average concentration levels of HMs were as follows: Chromium (Cr) (31.79 &plusmn; 37.9 mg/kg), Copper (Cu) (6.76 &plusmn; 12.54 mg/kg), Lead (Pb) (6.34 &plusmn; 14.55 mg/kg), and Zinc (Zn) (23.44 &plusmn; 84.43 mg/kg). The modelling accuracy, based on different evaluation criteria, showed that agricultural and industrial stations showed performance merit with goodness-of-fit ranges of 51&ndash;91% and 80&ndash;99%, respectively. This study concludes that AI models could be successfully applied for the rapid estimation of soil trace metals and related decision-making

Geochemical and Spatial Distribution of Topsoil HMs Coupled with Modeling of Cr Using Chemometrics Intelligent Techniques: Case Study from Dammam Area, Saudi Arabia

Unconsolidated earthen surface materials can retain heavy metals originating from different sources. These metals are dangerous to humans as well as the immediate environment. This danger leads to the need to assess various geochemical conditions of the materials. In this study, the assessment of topsoil materials&rsquo; contamination with heavy metals (HMs) was conducted. The material&rsquo;s representative spatial samples were taken from various sources: agricultural, industrial, and residential areas. The materials include topsoil, eolian deposits, and other unconsolidated earthen materials. The samples were analyzed using the ICP-OES. The obtained results based on the experimental procedure indicated that the average levels of the heavy metals were: As (1.21 &plusmn; 0.69 mg/kg), Ba (110.62 &plusmn; 262 mg/kg), Hg (0.08 &plusmn; 0.18 mg/kg), Pb (6.34 &plusmn; 14.55 mg/kg), Ni (8.95 &plusmn; 5.66 mg/kg), V (9.98 &plusmn; 6.08 mg/kg), Cd (1.18 &plusmn; 4.33 mg/kg), Cr (31.79 &plusmn; 37.9 mg/kg), Cu (6.76 &plusmn; 12.54 mg/kg), and Zn (23.44 &plusmn; 84.43 mg/kg). Subsequently, chemometrics modeling and a prediction of Cr concentration (mg/kg) were performed using three different modeling techniques, including two artificial intelligence (AI) techniques, namely, generalized neural network (GRNN) and Elman neural network (Elm NN) models, as well as a classical multivariate statistical technique (MST). The results indicated that the AI-based models have a superior ability in estimating the Cr concentration (mg/kg) than MST, whereby GRNN can enhance the performance of MST up to 94.6% in the validation step. The concentration levels of most metals were found to be within the acceptable range. The findings indicate that AI-based models are cost-effective and efficient tools for trace metal estimations from soil