Fishing gear selectivity of wire trap to limbat fish (Clarias nieuhofii) in swamp water Rimbo Ulu, Tebo Regency, Jambi Province

This study aims to analyze the level of selectivity of wire trap fishing gear against limbat fish (Clarias nieuhofii) in the swamp waters of Tebo Regency, Jambi Province. Data collection was carried out by catching trials using 3 units of wire traps with 30 replications, in June-August 2020. The research data were analyzed descriptively using the logistical selectivity model of the maximum likelihood method equation in the solver program from microsoft excel. The results showed that the wire traps were not selective for size but were selective for the limbat fish species (C.nieuhofii). The selectivity curve based on the logistical function shows that the probability of being caught by fish are at 22-48 mm body height, while the size of the fish that can escape has a maximum height of 34 mm or a length of 182 mm.

Contextualising the Pharmacist Practicing Clinical Pharmacy in South Africa – Do we Practice What we Preach?

Background: The National Department of Health published their Quality Standards for Healthcare Establishments in South Africa and introduced the National Health Insurance (NHI), with the pilot phase that commenced in 2012. The system requires an adequate supply of pharmaceutical personnel and the direct involvement of clinical pharmacists throughout the medication-use process to ensure continuity of care, minimised risk with increasing improvement of patient outcomes. The study aimed to provide insight into the pressing issues of clinical pharmacy practice in South Africa, and sets out to contextualise the current profile of the pharmacist performing clinical functions.Methods: The study used a quantitative, explorative, cross-sectional design. The population included pharmacists from private and public tertiary hospitals. A questionnaire was administered, using Typeform™. Ethics approval was obtained from relevant role-players. Categorical data were summarised using frequency counts and percentages; continuous data were summarised by mean values and standard deviations.Results: The sample size included 70 pharmacists (private sector n=59; public sector n=11). Most participants hold a BPharm degree (64%; n=70). No statistical significance was found between participants in private and public practice. Most pharmacist agreed (32% (private); n=59) and strongly agreed (45% (public); n=11) to have sufficient training to perform pharmaceutical care. The majority respondents felt that interventions made by the pharmacist improved the rational use of medicine (47% (private); n=59; 55% (public); n=11), that pharmacist interventions influence prescribing patterns (42% (private); n=59; 64% (public); n=11) and reduce polypharmacy (41% (private); n=59; 55% (public); n=11). Clinical functions performed most are evaluation of prescriptions (private 90%; public 82%) while the top logistical function for private is daily ordering of medication (40.7%), and public checking of ward stock (36%).Conclusion: Although not all pharmacists appointed in South Africa has completed the MPharm degree in clinical pharmacy, the pharmacists at ward level perform numerous clinical functions, even if only for a small part of their workday. This paper sets the way to standardise practices of clinical pharmacy in South Africa, with a reflection on the differences in practice in different institutions.

Estimate of the Maximum Limit of Total Cases of Infected Patients COVID-19

In this work, we present a method to estimate the maximum limit of total cases COVID-19 cases considering that the time in which the maximum number of new daily cases occurs corresponds to the inflection point of the curve described by the total number of cases that assumed to have a growth according to a logistical function in which the number of total cases at the inflection point will correspond to half of the maximum limit of total cases COVID-19. We estimate this maximum limit for China and South Korea, obtaining results compatible with the observations. And we also estimate for Italy, Germany, United Kingdom, United States and Spain.

A novel cohort analysis approach to determining the case fatality rate of COVID-19 and other infectious diseases

As the Coronavirus contagion develops, it is increasingly important to understand the dynamics of the disease. Its severity is best described by two parameters: its ability to spread and its lethality. Here, we combine a mathematical model with a cohort analysis approach to determine the range of case fatality rates (CFR). We use a logistical function to describe the exponential growth and subsequent flattening of COVID-19 CFR that depends on three parameters: the final CFR (L), the CFR growth rate (k), and the onset-to-death interval (t0). Using the logistic model with specific parameters (L, k and t0), we calculate the number of deaths each day for each cohort. We build an objective function that minimizes the root mean square error between the actual and predicted values of cumulative deaths and run multiple simulations by altering the three parameters. Using all of these values, we find out which set of parameters returns the lowest error when compared to the number of actual deaths. We were able to predict the CFR much closer to reality at all stages of the viral outbreak compared to traditional methods. This model can be used far more effectively than current models to estimate the CFR during an outbreak, allowing for better planning. The model can also help us better understand the impact of individual interventions on the CFR. With much better data collection and labeling, we should be able to improve our predictive power even further.

Microvascular pressure profile of serosal vessels of rat trachea

Pages H1303–H1304: S. T. Ballard, R. H. Nations, and A. E. Taylor. “Microvascular pressure profile of serosal vessels of rat trachea.” Measurements of microvessel diameter were overestimated because of a calibration error. The corrected vessel diameters are 0.56 times those originally reported. The corrected diameter ranges for arterioles, venules, and venular sinuses should be 6–53, 11–42, and 67–236 μm, respectively. When it is assumed that capillaries fall between 10-μm arterioles and venules, capillary pressures predicted by a four-parameter logistical function range from 34.5 and 12.7% of mean arterial pressure. Therefore, when the average large venular pressure was 5% of mean arterial pressure, fractional precapillary (systemic to 10-μm-diam arterioles), capillary (10-μm-diam arterioles to 10-μm-diam venules), and postcapillary (10-μm-diam venules to large venules) resistances would represent 69, 23, and 8%, respectively, of the total microvascular resistance. A revised Fig. 1 comparing experimental data with those of Nordin et al. (3), who measured microvascular pressure in rabbit tracheal mucosa, follows. (See PDF)

Microvascular pressure profile of serosal vessels of rat trachea

Pages H1303–H1304: S. T. Ballard, R. H. Nations, and A. E. Taylor. “Microvascular pressure profile of serosal vessels of rat trachea.” Measurements of microvessel diameter were overestimated because of a calibration error. The corrected vessel diameters are 0.56 times those originally reported. The corrected diameter ranges for arterioles, venules, and venular sinuses should be 6–53, 11–42, and 67–236 μm, respectively. When it is assumed that capillaries fall between 10-μm arterioles and venules, capillary pressures predicted by a four-parameter logistical function range from 34.5 and 12.7% of mean arterial pressure. Therefore, when the average large venular pressure was 5% of mean arterial pressure, fractional precapillary (systemic to 10-μm-diam arterioles), capillary (10-μm-diam arterioles to 10-μm-diam venules), and postcapillary (10-μm-diam venules to large venules) resistances would represent 69, 23, and 8%, respectively, of the total microvascular resistance. A revised Fig. 1 comparing experimental data with those of Nordin et al. (3), who measured microvascular pressure in rabbit tracheal mucosa, follows. (See PDF)