Sensitivity of Sinus Radiography Compared to Computed Tomogram: A Descriptive Cross-sectional Study from Western Region of Nepal

Introduction: Radiography of the paranasal sinuses is commonly used diagnostic modality. However, the trustworthiness of plain radiographic findings of paranasal sinuses is debatable. The intention of this study was to weigh the diagnostic soundness of plain radiograph of the paranasal sinuses to that of computed tomogram scan. Methods: This is a descriptive cross sectional study carried out in 110 participants in Department of Radiology of Gandaki Medical College from November 2017 to April 2018. Ethical approval is obtained from Institution review board (Ref. No.39/074/075). Sample size was calculated taking confidence level of 95%, expected prevalence of 14% and precision of 6.5% in population of 492098 in Province 4 of Nepal. Random sampling method was used. Data was enter in Statistical Package for the Social Sciences version 17 software and analysed. Results: A total of 110 participants are included in this study of which 62 (56.4%) are females and 48 (43.6%) are males with an overall mean age of 34.5 years. The commonly involved sinus was maxillary 56 (50.9%) followed by ethmoid 33 (30%) sinus. The overall sensitivity and specificity of detecting sinusitis by sinus radiography is higher for maxillary sinus (89.7% and 87%) followed by ethmoid (69.7% and 96.1%) and frontal (61.5% and 96.9%) sinuses. Conclusions: Sinus radiography is more sensitive for detecting pathologies in maxillary sinuses, while it is moderate for frontal, ethmoid sinuses and least for sphenoid sinuses. Diagnostic accuracy of computed tomogram scan is more, hence should be recommended to characterize the complex pathology and anatomy of the osteomeatal complex

Polyamide-polyamine cryptand as dicarboxylate receptor : dianion binding studies in the solid state, in solution, and in the gas phase

Polyamide-polyamine hybrid macrobicycle L is explored with respect to its ability to bind α,?-dicarboxylate anions. Potentiometric studies of protonated L with the series of dianions from succinate (suc) through glutarate (glu), α-ketoglutarate (kglu), adipate (adi), pimelate (pim), suberate (sub), to azelate (aze) have shown adipate preference with association constant value of K = 4900 M-1 in a HO/DMSO (50:50 v/v) binary solvent mixture. The binding constant increases from glu2- to adi2- and then continuously decreases with the length of the anion chain. Further, potentiometric studies suggest that hydrogen bonding between the guest anions and the amide/ammonium protons of the receptor also contributes to the stability of the associations along with electrostatic interactions. Negative-mode electrospray ionization of aqueous solutions of host-guest complexes shows clear evidence for the selective formation of 1:1 complexes. Single-crystal X-ray structures of complexes of the receptor with glutaric acid, α-ketoglutaric acid, adipic acid, pimelic acid, suberic acid, and azelaic acid assist to understand the observed binding preferences. The solid-state structures reveal a size/shape complementarity between the host and the dicarboxylate anions, which is nicely reflected in the solution state binding studies

Comparison of in vitro acaricidal effects of commercial preparations of cypermethrin and fenvalerate against Rhipicephalus (Boophilus) annulatus

Commercially available preparations of cypermethrin (Clinar and Ectomin) and fenvalerate (Flytik and Ticomax, 20% E.C) were compared for their acaricidal activity against Rhipicephalus (Boophilus) annulatus using adult immersion test. Adult tick mortality was higher with Ectomin compared to Clinar. Complete eclosion blocking was observed at all the tested concentrations with Ectomin while it was observed only at the highest concentration tested for Clinar. Compared to Flytik, adult tick mortality was higher with Ticomax at the tested concentrations. Complete blocking of hatching of laid ova was observed with Flytik at the highest concentration tested. At the manufacture recommended dosage of 200 ppm Ectomin elicited 93.37 per cent inhibition of fecundity, while it was 91.7 per cent for Clinar. For fenvalerate, the recommended concentration was 1200 ppm at which Ticomax showed 86 per cent and Flytik produced 80.05 per cent inhibition of fecundity respectively

Acaricidal Activity of Petroleum Ether Extract of Leaves of Tetrastigma leucostaphylum

The acaricidal activity of the petroleum ether extract of leaves of Tetrastigma leucostaphylum (Dennst.) Alston (family: Vitaceae) against Rhipicephalus (Boophilus) annulatus was assessed using adult immersion test (AIT). The per cent of adult mortality, inhibition of fecundity, and blocking of hatching of eggs were studied at different concentrations. The extract at 10% concentration showed 88.96% inhibition of fecundity, 58.32% of adult tick mortality, and 50% inhibition of hatching. Peak mortality rate was observed after day 5 of treatment. Mortality of engorged female ticks, inhibition of fecundity, and hatching of eggs were concentration dependent. The LC50 value of the extract against R. (B.) annulatus was 10.46%. The HPTLC profiling of the petroleum ether extract revealed the presence of at least seven polyvalent components. In the petroleum ether extract, nicotine was identified as one of the components up to a concentration of 5.4%. However, nicotine did not reveal any acaricidal activity up to 20000 ppm (2%). Coconut oil, used as diluent for dissolving the extract, did not reveal any acaricidal effects. The results are indicative of the involvement of synergistic or additive action of the bioactive components in the tick mortality and inhibition of the oviposition

Introducing v0.5 of the AI Safety Benchmark from MLCommons

This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Introducing v0.5 of the AI Safety Benchmark from MLCommons

This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark