Abstract:The current economic environment in the Indian manufacturing industry is offering a perfect opportunity to SMEs of this country to develop and grow by acting as suppliers of large multinational original equipment manufacturers (OEMs). To meet the challenge of offering high standards of quality, cost and delivery (QCD) to these multinational OEMs, Indian manufacturing SMEs must implement effective approaches, such as lean manufacturing, to continually and systematically improve their operations. However, the academic literature indicates that the adoption of lean manufacturing and some of its tools, such as value stream mapping (VSM), by Indian manufacturing SMEs is feeble, or in many cases lean initiatives have been unsuccessful. This paper presents a successful application of VSM in an Indian manufacturing SME. The results of the study and operational improvements achieved indicate that the application of VSM is an effective 42 A. Saboo et al.strategy for organisations of this type to improve their processes and thus meet their current challenges. This paper contributes by providing empirical evidence of the application of VSM in India and thus it can be used as a guiding reference for managers and engineers to undertake specific process improvement projects, in their organisations, similar to the one presented in this paper.Keywords: India; lean operations; manufacturing; SME; value stream mapping; VSM.Reference to this paper should be made as follows: Saboo, A., Garza-Reyes, J.A., Er, A. and Kumar, V. (2014) 'A VSM improvement-based approach for lean operations in an Indian manufacturing SME', Int.

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Purpose While more advanced COVID-19 necessitates medical interventions and hospitalization, patients with mild COVID-19 do not require this. Identifying patients at risk of progressing to advanced COVID-19 might guide treatment decisions, particularly for better prioritizing patients in need for hospitalization. Methods We developed a machine learning-based predictor for deriving a clinical score identifying patients with asymptomatic/mild COVID-19 at risk of progressing to advanced COVID-19. Clinical data from SARS-CoV-2 positive patients from the multicenter Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS) were used for discovery (2020-03-16 to 2020-07-14) and validation (data from 2020-07-15 to 2021-02-16). Results The LEOSS dataset contains 473 baseline patient parameters measured at the first patient contact. After training the predictor model on a training dataset comprising 1233 patients, 20 of the 473 parameters were selected for the predictor model. From the predictor model, we delineated a composite predictive score (SACOV-19, Score for the prediction of an Advanced stage of COVID-19) with eleven variables. In the validation cohort (n = 2264 patients), we observed good prediction performance with an area under the curve (AUC) of 0.73 ± 0.01. Besides temperature, age, body mass index and smoking habit, variables indicating pulmonary involvement (respiration rate, oxygen saturation, dyspnea), inflammation (CRP, LDH, lymphocyte counts), and acute kidney injury at diagnosis were identified. For better interpretability, the predictor was translated into a web interface. Conclusion We present a machine learning-based predictor model and a clinical score for identifying patients at risk of developing advanced COVID-19.

Background COVID‐19‐associated pulmonary aspergillosis (CAPA) has been reported as an important cause of mortality in critically ill patients with an incidence rate ranging from 5% to 35% during the first and second pandemic waves. Objectives We aimed to evaluate the incidence, risk factors for CAPA by a screening protocol and outcome in the critically ill patients during the third wave of the pandemic. Patients/Methods This prospective cohort study was conducted in two intensive care units (ICU) designated for patients with COVID‐19 in a tertiary care university hospital between 18 November 2020 and 24 April 2021. SARS‐CoV‐2 PCR‐positive adult patients admitted to the ICU with respiratory failure were included in the study. Serum and respiratory samples were collected periodically from ICU admission up to CAPA diagnosis, patient discharge or death. ECMM/ISHAM consensus criteria were used to diagnose and classify CAPA cases. Results A total of 302 patients were admitted to the two ICUs during the study period, and 213 were included in the study. CAPA was diagnosed in 43 (20.1%) patients (12.2% probable, 7.9% possible). In regression analysis, male sex, higher SOFA scores at ICU admission, invasive mechanical ventilation and longer ICU stay were significantly associated with CAPA development. Overall ICU mortality rate was higher significantly in CAPA group compared to those with no CAPA (67.4% vs 29.4%, p < .001). Conclusions One fifth of critically ill patients in COVID‐19 ICUs developed CAPA, and this was associated with a high mortality.

The disease course of children with coronavirus disease 2019 (COVID‐19) seems milder as compared with adults, however, actual reason of the pathogenesis still remains unclear. There is a growing interest on possible relationship between pathogenicity or disease severity and biomarkers including cytokines or chemokines. We wondered whether these biomarkers could be used for the prediction of the prognosis of COVID‐19 and improving our understanding on the variations between pediatric and adult cases with COVID‐19. The acute phase serum levels of 25 cytokines and chemokines in the serum samples from 60 COVID‐19 pediatric ( n = 30) and adult cases ( n = 30) including 20 severe or critically ill, 25 moderate and 15 mild patients and 30 healthy pediatric ( n = 15) and adult ( n = 15) volunteers were measured using commercially available fluorescent bead immunoassay and analyzed in combination with clinical data. Interferon gamma‐induced protein 10 (IP‐10) and macrophage inflammatory protein (MIP)−3β levels were significantly higher in patient cohort including pediatric and adult cases with COVID‐19 when compared with all healthy volunteers ( p ≤ .001 in each) and whereas IP‐10 levels were significantly higher in both pediatric and adult cases with severe disease course, MIP‐3β were significantly lower in healthy controls. Additionally, IP‐10 is an independent predictor for disease severity, particularly in children and interleukin‐6 seems a relatively good predictor for disease severity in adults. IP‐10 and MIP‐3β seem good research candidates to understand severity of COVID‐19 in both pediatric and adult population and to investigate possible pathophysiological mechanism of COVID‐19.

Background/aim: The COVID-19 Pandemic originated in Wuhan, China, in December 2019 and became one of the worst global health crises ever. While struggling with the unknown nature of this novel coronavirus, many researchers and groups attempted to project the progress of the pandemic using empirical or mechanistic models, each one having its drawbacks. The first confirmed cases were announced early in March, and since then, serious containment measures have taken place in Turkey. Materials and methods: Here, we present a different approach, a Bayesian negative binomial multilevel model with mixed effects, for the projection of the COVID-19 pandemic and apply this model to the Turkish case. The model source code is available at https://github.com/kansil/covid-19. We predicted confirmed daily cases and cumulative numbers for June 6th to June 26th with 80%, 95% and 99% prediction intervals (PI). Results: Our projections showed that if we continued to comply with measures and no drastic changes are seen in diagnosis or management protocols, the epidemic curve would tend to decrease in this time interval. Also, the predictive validity analysis suggests that proposed model projections should be in the 95% PI band for the first 12 days of the projections. Conclusion: We expect that drastic changes in the course of the COVID-19 in Turkey will cause the model to suffer in predictive validity, and this can be used to monitor the epidemic. We hope that the discussion on these projections and the limitations of the epidemiological forecasting will be beneficial to the medical community, and policymakers.

Abstract:The adoption of Lean concepts beyond the manufacturing sector has been increasing recently. In this line, its scope has been expanded to the mining industry under the realisation of the need for productivity improvements and a leverage for efficient operations. Limited research exists regarding Lean implementation in the mining industry in a comprehensive and structured way. This paper therefore follows a systematic approach to review the current literature to identify Lean implementation patterns in the mining sector, its scope, challenges, and limitations. The results reveal the limited utilisation of Lean in the mining sector, and that there is a lack of coherent and conceptual models to guide the implementation of Lean in this industry. Hence, the research proposes a framework for Lean implementation in the mining industry.

There is a cumulative evidence suggesting COVID-19 victims are prone to COVID-19 associated pulmonary aspergillosis (CAPA).COVID-19 itself and immunomodulatory medications, such as corticosteroids and tocilizumab, compromise the immune system to an extent that opportunistic infections complicate the course further. 1 In this letter, we aimed to highlight the relationship between inflammation and voriconazole trough levels in COVID-19 patients.Voriconazole is recommended as the first-line agent for the treatment of invasive pulmonary aspergillosis (IPA). 1 Voriconazole is metabolized with cytochrome P450 (CYP450) isoenzymes (mainly with CYP2C19 and lesser extent with CYP3A4) to voriconazole Noxide. Voriconazole reaches to steady-state trough concentrations approximately at the fifth day of administration. Therapeutic drug monitoring for voriconazole is recommended because of the narrow therapeutic index. 2 Voriconazole dose for IPA is recommended as 4 mg/kg every 12 h for maintenance, followed by 6 mg/kg loading dose every 12 h in the first day. It was recommended that the trough level of voriconazole should be between 1.5 and 5.5 mg/L. Voriconazole trough level over 4.5-6 mg/L has been associated with hepatotoxicity. 3 The common side effects of voriconazole were defined as visual disturbances, fever, nausea, rash, vomiting, chills, headache, abnormal liver function tests, and hallucinations. 4 Since the beginning of COVID-19 pandemic, a total of 13 COVID-19 patients were treated with voriconazole for CAPA in our university hospital based on mycological, clinical, and radiological findings. Among 13 patients, 12 (92.3%) were critically ill. All patients, except one, had bacterial or viral coinfection in addition to CAPA.Plasma voriconazole level measurements were performed with liquid chromatography-triple quadrupole mass spectrometer (Shimadzu LCMS-8040). Two of those had a DDI with voriconazole (with 500-mg intravenous clarithromycin twice daily and 80-mg oral omeprazole daily), which might contribute to high voriconazole trough levels due to their inhibitory effect on CYP450 isoenzymes. However, the voriconazole level remained elevated despite discontinuation of clarithromycin in one patient, suggesting a different mechanism. In five (41.7%) critically ill patients, the trough level of voriconazole remained in the supratherapeutic range despite a dose reduction of 100 mg/day. In summary, no associated factor was detected for the explanation of higher voriconazole trough levels in 12 critically ill patients. It was observed that COVID-19 patients were more prone to high voriconazole levels than non-COVID-19 patients. In four of 13 non-COVID-19 patients, the voriconazole trough level was