Globally local: Hyper-local modeling for accurate forecast of COVID-19.

IMPORTANCE: Assumption of a well-mixed population during modeling is often erroneously made without due analysis of its validity. Ignoring the importance of the geo-spatial granularity at which the data is collected could have significant implications on the quality of forecasts and the actionable clinical recommendations that are based on it. OBJECTIVE: This paper's primary objective is to test the hypothesis that the characteristic dynamics defining the trajectory of the pandemic in a region is lost when the data is aggregated and modeled at higher geo-spatial levels. DESIGN: We use publicly available confirmed SARS-CoV-2 cases and deaths from January 1st, 2020 to August 3rd, 2020 in the United States at different geo-spatial granularities to conduct our experiments. To understand the impact of this hypothesis, the output of this study was implemented in Tampa General Hospital (TGH) to provide resource demand forecast. RESULTS: The Mean Absolute Percentage Error (MAPE) in the forecast confirmed cases can be 30% higher for modeling at the state-level than aggregating model results at the scale of counties or clusters of counties. Similarly, modeling at a state-level and crafting policy decisions based on them may not be effective - county-level forecasts made by partitioning state-level forecasts are 3x worse for confirmed cases and 20x worse for deaths relative to the same model at the county level. By leveraging these results, TGH was able to accurately allocate clinical resources to tackle COVID-19 cases, continue elective surgical procedures largely uninterrupted and avoid costly construction of overflow capacity in the first two epidemic waves. CONCLUSIONS AND RELEVANCE: Accurate forecasting at the county level requires hyper-local modeling with county resolution. State-level modeling does not accurately predict community spread in smaller sub-regions because state populations are not well mixed, resulting in large prediction errors. Actionable decisions such as deciding whether to cancel planned surgeries or construct overflow capacity require models with local specificity.

ISO 9001:2015 Versus ICH Q10 - A Comparison.

Do the changes to ISO 9001 from the 2008 version to the 2015 version warrant revision of ICH Q10? Or does ICH Q10 still meet the ISO 9001 principles? In 2008, the International Conference on Harmonisation (ICH) issued guideline ICH Q10, describing a model for a pharmaceutical quality system (PQS) that can be implemented throughout the different stages of a product life cycle. Explicitly, the guideline was not intended to create any new expectations beyond the existing regulatory requirements. ICH Q10 was founded on principles established by the International Organization for Standardization (ISO) describing a model for the structure of a quality management system (QMS). Since 1987, these principles were codified in the ISO 9000 series of quality standards, for example, as revised in ISO 9001:2008. ICH Q10 also incorporated applicable good manufacturing practice (GMP) regulations and complemented the existing ICH Q8 (R2) "Pharmaceutical Development" and ICH Q9 "Quality Risk Management" guidelines. ICH Q10 represents a harmonized model for a PQS that can be implemented throughout the different stages of a product life cycle. In 2015, ISO published ISO 9001:2015, a significant revision to the ISO 9001 QMS standard. This 2015 version contained extensive changes and a new structure. This revision to ISO 9001 raised the question of whether ICH Q10 should be reviewed and potentially revised, and whether ICH Q10 continues to meet the ISO 9000 principles. This article assessed whether the changes to the ISO 9001:2015 standard could make a revision of the ICH Q10 guideline necessary and whether ICH Q10 still represents a current model of a pharmaceutical quality management system.

Evaluation and elimination of inhibitory effects of salts and heavy metal ions on biodegradation of Congo red by Pseudomonas sp. mutant.

In this study, it was attempted to evaluate the influences and also recommended some elimination methods for inhibitory effects offered by salts and heavy metal ions. Congo red dye solution treated with mutant Pseudomonas sp. was taken as a model system for study. The salts used in this study are NaCl, CaCl(2) and MgSO(4)· 7H(2)O. Though the growth was inhibited at concentrations above 4 g/l, toleration was achieved by acclimatization process. In case of heavy metal ions, Cr (VI) showed low inhibition up to 500 mg/l of concentration, compared to Zn (II) and Cu (II). It was due to the presence of chromium reductase enzyme which was confirmed by SDS-PAGE. Zn (II) and Cu (II) ion inhibitions were eliminated by chelation with EDTA. The critical ion concentrations obtained as per Han-Levenspiel model for Cr (VI), Zn (II) and Cu (II) were 0.8958, 0.3028 and 0.204 g/l respectively.

Degradation of Tectilon Yellow 2G by hybrid technique: combination of sonolysis and biodegradation using mutant Pseudomonas putida.

Degradation of Tectilon Yellow 2G (TY2G), an azo dye has been studied by hybrid technique involving pretreatment by sonochemical method and further biological treatment by Pseudomonas putida mutant. Pretreatment experiments were carried out by sonolysis of the dye solution at different concentrations (100-1000 mg/L). Wild type Gram-negative P. putida species isolated from the textile effluent contaminated soil, which was found to be effective towards dye degradation, has been acclimatized so as to consume TY2G as the sole source of nutrition. Mutant strain was obtained from the acclimatized species by random mutagenesis using the chemical mutagen ethidium bromide for various time intervals (6-30 min). The optimum mutagenesis exposure time for obtaining the most efficient species for dye degradation was found to be 18 min. An efficient mutant strain P. putida ACT 1 has been isolated and was used for growth experiments. The mutant strain showed a better growth compared to the wild strain. The substrate utilization kinetics has been modeled using Monod and Haldane model equations of which the Haldane model provided a better fit. The enzyme kinetics of the mutant and wild species was obtained using Michaelis-Menten equation. The mutated species showed better enzyme kinetics towards the degradation of TY2G.