Search Results (55)

Background: Long COVID can develop in individuals who have had COVID-19, regardless of the severity of their initial infection or the treatment they received. Several studies have examined the prevalence and manifestation of symptom phenotypes to comprehend the pathophysiological mechanisms associated with these symptoms. Numerous articles outlined specific approaches for multidisciplinary management and treatment of these patients, focusing primarily on those with mild acute illness. The various management models implemented focused on a patient-centered approach, where the specialists were positioned around the patient. On the other hand, the created pathways do not consider the possibility of symptom clusters when determining how to define diagnostic algorithms. Methods: This retrospective longitudinal study took place at the “Fondazione IRCCS Policlinico San Matteo”, Pavia, Italy (SMATTEO) and at the “Ospedale di Cremona”, ASST Cremona, Italy (CREMONA). Information was retrieved from the administrative data warehouse and from two dedicated registries. We included patients discharged with a diagnosis of severe COVID-19, systematically invited for a 3-month follow-up visit. Unsupervised machine learning was used to identify potential patient phenotypes. Results: Three hundred and eighty-two patients were included in these analyses. About one-third of patients were older than 65 years; a quarter were female; more than 80% of patients had multi-morbidities. Diagnoses related to the circulatory system were the most frequent, comprising 46% of cases, followed by endocrinopathies at 20%. PCA (principal component analysis) had no clustering tendency, which was comparable to the PCA plot of a random dataset. The unsupervised machine learning approach confirms these findings. Indeed, while dendrograms for the hierarchical clustering approach may visually indicate some clusters, this is not the case for the PAM method. Notably, most patients were concentrated in one cluster. Conclusions: The extreme heterogeneity of patients affected by post-acute sequelae of SARS-CoV-2 infection (PASC) has not allowed for the identification of specific symptom clusters with the most recent statistical techniques, thus preventing the generation of common diagnostic-therapeutic pathways. Full article

Introduction: Postreperfusion syndrome (PRS) is associated with complications following liver transplantation (LT). Mannitol may play a role in attenuating PRS as a free radical scavenger. This study aimed to evaluate the association between intraoperative mannitol administration and the incidence of PRS and postoperative acute kidney injury (AKI) in LT. Methods: A retrospective analysis of adult liver-only transplantation between August 2019 and January 2023 at the Mount Sinai Hospital was performed. Patients in the mannitol group received 25G of the drug intravenously prior to reperfusion. Any recipients with pre-existing renal diagnoses were excluded. Demographic, laboratory, intraoperative, and hospital course data were extracted from an institutional data warehouse. Multivariable logistic regressions were used to evaluate the association between mannitol administration and PRS, AKI, early allograft dysfunction, and postoperative cardiac complications. Negative binomial regression was used to evaluate the association with postoperative length of stay (LOS) and ICU LOS. Results: 495 LT cases were included. A total of 81 patients received mannitol before graft reperfusion, while 414 patients did not. The incidence of PRS in patients who received mannitol was 13% and 17% for those who did not receive mannitol (p = 0.53). Additionally, 79% of patients who received mannitol experienced AKI at 7 days, compared to 73% in those who did not receive mannitol (p = 0.48). In the multivariable regression models, mannitol administration was not associated with decreased incidence of PRS or postoperative AKI. It was, however, associated with increased postoperative cardiac complications (risk-adjusted odds ratio 2.70, 95% confidence interval 1.15–6.14, p = 0.02). Conclusions: Mannitol administration during LT was not an effective therapy for reducing PRS or postoperative AKI. Full article

Quality risk management, commonly known as QRM, is designed to systematically assess, control, communicate, and review potential risks at every stage of the pharmaceutical manufacturing process. The preservation of consistent product quality across the entirety of the product’s life cycle is of paramount importance. The aim of this article is to formulate a best practice guide that will assist pharmaceutical manufacturers in comprehending and implementing the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) Q9: quality risk management principles. A widely recognized methodology for defining and monitoring risk mitigation strategies within the pharmaceutical sector is the Failure Mode and Effects Analysis (FMEA). ICH Q9 does not, however, offer detailed instructions for applying FMEA to real-world pharmaceutical situations. We previously provided real-world case studies that identify and mitigate risks in the early stages of the manufacturing process of sterile products, such as (1) supply chain and procurement; (2) logistics and warehousing; (3) raw material dispensing; (4) glass bottle washing and handling; (5) product filling; and (6) final product receiving and handling. The final steps of the sterile manufacturing process are the subject of the case study we present in this paper. We identify and control the risks related to (I) product sterilization; (II) product inspection, labeling, and packaging; (III) the finished product’s transfer to storage; and (IV) storing finished products in a warehouse. In order to maximize decision-making and reduce the risk of regulatory noncompliance, this case study describes a proactive strategy for the identification, management, and communication of risks associated with crucial tasks. While each organization’s products and methods are distinct, with varying tolerances for risk, certain stages and associated risks are common. Consequently, the examples provided here offer relevant insights into any pharmaceutical production environment. Managing sustainability-related risks and ensuring the transparency of pharmaceutical company operations are key tasks of success today. These risks, if not managed, will cause serious problems and a negative reputation, as well as environmental and public impact. Full article

This study investigates the transformation of e-commerce warehouse operations by integrating Lean Six Sigma tools to enhance efficiency and sustainability. Beginning with Value Stream Mapping (VSM) to identify inefficiencies, followed by a Hoshin Kanri plan to align improvement initiatives with strategic objectives, the study implemented measures such as pallet pooling, process standardization, automation in inspection and picking, layout optimization, and Kanban systems for continuous improvement. A case study of a local e-commerce warehouse specializing in medical devices and healthcare products identified 29 activities across receiving, inspection, storing, picking, packing, and shipping, highlighting inefficiencies addressed through Lean-driven initiatives. These efforts resulted in a 23% reduction in total lead time, doubled value-added time, and significant improvements in inspection, picking, packing, and automation, reducing delays, lowering costs, and enhancing workflow. The study fills a gap in the literature by integrating multiple Lean tools and utilizing the Critical to Quality (CTQ) matrix to ensure sustainable improvements in e-commerce warehousing, emphasizing the strategic value of Lean Six Sigma in creating efficient, customer-focused operations. Full article

Background/Objectives: This study evaluated comparative overall survival (OS) of United States veterans with unresectable hepatocellular carcinoma (uHCC) receiving first-line (1L) atezolizumab plus bevacizumab vs. sorafenib or lenvatinib, overall and across racial and ethnic groups. Methods: In this retrospective study, patients with uHCC who initiated atezolizumab plus bevacizumab (post-2020) or sorafenib or lenvatinib (post-2018) were identified from the Veterans Health Administration National Corporate Data Warehouse (1 January 2017–31 December 2022). Patient characteristics were evaluated in the year prior to 1L treatment initiation. Kaplan–Meier and multivariable Cox regression methods were used to compare OS starting from treatment between cohorts, both overall and by race and ethnicity. Results: Among the 1874 patients included, 405 (21.6%) received 1L atezolizumab plus bevacizumab, 1016 (54.2%) received sorafenib, and 453 (24.2%) received lenvatinib, with a median follow-up time of 8.5, 7.6, and 8.2 months, respectively. Overall, patients receiving atezolizumab plus bevacizumab had longer unadjusted median OS (12.8 [95% CI: 10.6, 17.1] months) than patients receiving sorafenib (8.0 [7.1, 8.6] months) or lenvatinib (9.5 [7.8, 11.4] months; both log-rank p < 0.001). After adjustment, atezolizumab plus bevacizumab was associated with a reduced risk of death by 30% vs. sorafenib (adjusted HR: 0.70 [95% CI: 0.60, 0.82]) and by 26% vs. lenvatinib (0.74 [0.62, 0.88]; both p < 0.001). OS trends in the White, Black, and Hispanic patient cohorts were consistent with that of the overall population. Conclusions: Atezolizumab plus bevacizumab was associated with improved survival outcomes compared with sorafenib and lenvatinib in patients with uHCC, both overall and across racial and ethnic subgroups. Full article

The primary objective of a depalletizing system is to automate the process of detecting and locating specific variable-shaped objects on a pallet, allowing a robotic system to accurately unstack them. Although many solutions exist for the problem in industrial and manufacturing settings, the application to small-scale scenarios such as retail vending machines and small warehouses has not received much attention so far. This paper presents a comparative analysis of four different computer vision algorithms for the depalletizing task, implemented on a Raspberry Pi 4, a very popular single-board computer with low computer power suitable for the IoT and edge computing. The algorithms evaluated include the following: pattern matching, scale-invariant feature transform, Oriented FAST and Rotated BRIEF, and Haar cascade classifier. Each technique is described and their implementations are outlined. Their evaluation is performed on the task of box detection and localization in the test images to assess their suitability in a depalletizing system. The performance of the algorithms is given in terms of accuracy, robustness to variability, computational speed, detection sensitivity, and resource consumption. The results reveal the strengths and limitations of each algorithm, providing valuable insights for selecting the most appropriate technique based on the specific requirements of a depalletizing system. Full article

This study investigated the usage patterns and impact of therapeutic drug monitoring (TDM) for risperidone and paliperidone in patients diagnosed with schizophrenia, utilizing retrospective real-world data sourced from a single center’s Clinical Data Warehouse. Our study cohort comprised patients diagnosed with schizophrenia undergoing treatment with either risperidone or paliperidone. Data on demographic characteristics, comorbidities, medication utilization, and clinical outcomes were collected. Patients were categorized into two groups: those undergoing TDM and those not undergoing TDM. Additionally, within the TDM group, patients were further stratified based on their risperidone and paliperidone concentrations relative to the reference range. The findings revealed that patients in the TDM group received higher risperidone and paliperidone doses (320 mg/day and 252 mg/day, p = 0.0045) compared to their non-TDM counterparts. Nevertheless, no significant disparities were observed in hospitalization rates, duration of hospital stays, or compliance between the two groups (p = 0.9082, 0.5861, 0.7516, respectively). Subgroup analysis within the TDM cohort exhibited no notable distinctions in clinical outcomes between patients with concentrations within or surpassing the reference range. Despite the possibility of a selection bias in assigning patients to the groups, this study provides a comprehensive analysis of TDM utilization and its ramifications on schizophrenia treatment outcomes. Full article

In today’s competitive world, customers expect their demands to be met at the shortest possible time, while manufacturers aspire to deliver the orders within a convenient time and at a minimum cost. Thus, manufacturers are compelled to seek ways of lowering the costs of their services in order to satisfy customers and survive the competition in their respective industries. This research paper investigates a multi-product problem in a two-echelon supply chain consisting of a single manufacturer and several retailers. The main objective of this research is to develop and present a multi-product optimization model in which retailers receive their orders through discrete delivery and surplus manufactured goods are stored in the manufacturer’s warehouse. The objective function of the mathematical model in the economic dimension includes the minimization of the total supply chain costs and the maximization of profit. The retailers in this model place new orders when their inventory level drops to zero, and the manufacturer responds to the retailers’ orders at the same time as it begins processing each product. After delivering the last set of orders, the manufacturer stores surplus items in its warehouse in case the retailers place new orders. This optimization problem is modeled using mixed integer nonlinear programming, while numerical scenarios are coded using the MATLAB software which helps estimate the total cost within a short time. Finally, a sensitivity analysis is performed to determine the effects of a number of factors on the total cost, including problem parameters, demand and production rates, the production quantity, and the number of times the manufacturing machines are operated at each production cycle. Full article

Intermodal terminals and warehouses operate in different countries and deliver specific services to their customers. For many clients, it is important to receive a full set of the logistics services delivered by a single operator. However, individual intermodal terminals and warehouses may face challenges with providing these services, e.g., just-in-time goods delivery, goods distribution, cargo handling in non-standard situations, and others. In such cases, the cooperation between logistics companies may be required to organize the comprehensive service of cargo within supply chains. One of the possible solutions is to integrate transport and logistics services providers, establishing their cooperation within one virtual logistics center. The aim of this article is to justify theoretically the possibility of creating such a center by combining services performed by the intermodal terminals and warehouses already in operation under a single entity, in order to minimize the cost of logistics services and the time of goods delivery, as well as to create a comprehensive range of logistics services needed by customers. The relevance of the article and the novelty of the idea are associated with justification of the possibility of combining the activities of intermodal terminals and warehouses located separately in the region in order to improve the logistical service of customers. The theoretical basis for creating a virtual logistics center is based on graph theory methods. The article presents a theoretical model, based on a system of edges and vertices of the graph tree, which corresponds to the activities performed by separately located intermodal terminals and individual warehouses. The discussion is focused on the current problems of creating virtual logistics centers. The research results may be interesting for the managers of intermodal terminals, warehouses, and logistics centers, as well as other decision-makers involved in supply chains implementation and development. Full article

Background: Individuals with serious mental illness (SMI) experience barriers to accessing and engaging with healthcare, which may have been exacerbated during the emergence of the global pandemic and the rapid shift to telemedicine platforms, substantially decreasing healthcare utilization for non-COVID-19 disorders. Important repercussions on morbidity and mortality may be seen in the months and years to come, which may disproportionately affect high-risk populations, such as patients with SMI, with reduced access to technology platforms. In this study, we explored the impact of the pandemic on healthcare utilization and all-cause mortality rate in SMI compared to non-SMI individuals for the months of March–September 2020 and the same two quarters in 2019. Methods: Data were obtained from the VA Corporate Data Warehouse (CDW), a data repository from clinical and administrative VA systems. The sample included veterans with ≥1 outpatient clinical encounter nationally between 1 January 2019 and 31 December 2020. Results: The cohort for this study included 1,018,047 veterans receiving care through the Veterans Health Administration between 2019 and 2020. Of those, 339,349 had a diagnosis of SMI. Patients with SMI had a significantly larger pre–post-pandemic decrease in outpatient (49.7%, p < 0.001), inpatient (14.4%, p < 0.001), and ED (14.5%, p < 0.001) visits compared to non-SMI patients. Overall, 3752 (1.59%) veterans without SMI and 4562 (1.93%) veterans with SMI died during our observation period. Veterans without SMI who died during the observation period were more likely to have had a positive COVID-19 test compared to veterans with SMI. Unadjusted analyses showed that veterans with SMI were approximately 2.5 times more likely to die than veterans without SMI during the first 6 months of the pandemic, compared to the same two quarters of the previous year. However, after adjustment by pertinent covariates, the predictors associated with an increased risk of death from SMI were older age, being male, a higher CAN score, more inpatient stays in the pre period compared to post, and a positive COVID-19 test. Discussion: Consistent with our initial hypothesis, all the indices of healthcare utilization, namely the number of outpatient, inpatient, and ED visits, significantly decreased between pre- and post-pandemic and did more so for veterans with SMI, despite having more chronic medical illnesses and being prescribed more medications than veterans without SMI. On the other hand, while mortality was greater post-pandemic, factors such as age, morbidity, and having a positive COVID-19 test predicted mortality above and beyond having an SMI diagnosis. Full article

Farming is a major sector required for any nation to become self-sustainable. Quality seeds heavily influence the effectiveness of farming. Seeds cultivated by breeders pass through several entities in order to reach farmers. The existing seed supply chain is opaque and intractable, which not only hinders the growth of crops but also makes the life of a farmer miserable. Blockchain has been widely employed to enable fair and secure transactions between farmers and buyers, but concerns related to transparency and traceability in the seed supply chain, counterfeit seeds, middlemen involvement, and inefficient processes in the agricultural ecosystem have not received enough attention. To address these concerns, a blockchain-based solution is proposed that brings breeders, farmers, warehouse owners, transporters, and food corporations to a single platform to enhance transparency, traceability, and trust among trust-less parties. A smart contract updates the status of seeds from a breeder from $submitted$ to $approved$. Then, a non-fungible token (NFT) corresponding to approved seeds is minted for the breeder, which records the date of cultivation and its owner (breeder). The NFT enables farmers to keep track of seeds right from the date of their cultivation and their owner, which helps them to make better decisions about picking seeds from the correct owner. Farmers directly interact with warehouses to purchase seeds, which removes the need for middlemen and improves the trust among trust-less entities. Furthermore, a tender for the transportation of seeds is auctioned on the basis of the priority location $lo{c}_p$, Score, and bid_amount of every transporter, which provides a fair chance to every transporter to restrict the monopoly of a single transporter. The proposed system achieves immutability, decentralization, and efficiency inherently from the blockchain. We implemented the proposed scheme and deployed it on the Ethereum network. Smart contracts deployed over the Ethereum network interact with React-based web pages. The analysis and results of the proposed model indicate that it is viable and secure, as well as superior to the current seed supply chain system. Full article

Dispatching, receiving, and transporting goods involve a large amount of manual effort. Within a logistics supply chain, a wide variety of transported goods need to be handled, recognized, and checked at many different points. Effective planning of automated guided vehicle (AGV) transportation can reduce equipment energy consumption and shorten task completion time. As the need for efficient warehouse logistics has increased in manufacturing systems, the use of AGVs has also increased to reduce working time. These processes hold automation potential, which we can exploit by using computer vision techniques. We propose a method for the complete automation of box recognition, covering both the types and quantities of boxes. To do this, an ELAN and GhostConv-based YOLO network (EGCY-Net) is proposed with a Conv-GhostConv Stack (CGStack) module and an ELAN-GhostConv Network (EGCNet). To enhance inter-channel relationships, the CGStack module captures complex patterns and information in the image by using ghost convolution to increase the model inference speed while retaining the ability to capture spatial features. EGCNet is designed and constructed based on ELAN and the CGStack module to capture and utilize hierarchical features efficiently in layer aggregation. Additionally, the proposed methodology involves the creation of a dataset comprising images of boxes taken in warehouse settings. The proposed system is realized on the NVIDIA Jetson Nano platform, using an Arducam IMX477 camera. To evaluate the proposed model, we conducted experiments with our own dataset and compared the results with some state-of-the-art (SOTA) models. The proposed network achieved the highest detection accuracy with the fewest parameters compared to other SOTA models. Full article

Effective inventory management is crucial for businesses to balance minimizing holding costs while optimizing ordering strategies. Monthly or sporadic orders over time may lead to high ordering or holding costs, respectively. In this study, we introduce two novel algorithms designed to optimize ordering replenishment quantities, minimizing total replenishment, and holding costs over a planning horizon for both partially loaded and fully loaded trucks. The novelty of the first algorithm is that it extends the classical Wagner–Whitin approach by incorporating various additional cost elements, stock retention considerations, and warehouse capacity constraints, making it more suitable for real-world problems. The second algorithm presented in this study is a variation of the first algorithm, with its contribution being that it incorporates the requirement of several suppliers to receive order quantities that regard only fully loaded trucks. These two algorithms are implemented in Python, creating the software tool called “Inventory Cost Minimizing tool” (ICM). This tool takes relevant data inputs and outputs optimal order timing and quantities, minimizing total costs. This research offers practical and novel solutions for businesses seeking to streamline their inventory management processes and reduce overall expenses. Full article

We discuss two queueing-inventory systems with catastrophes in the warehouse. Catastrophes occur according to the Poisson process and instantly destroy all items in the inventory. The arrivals of the consumer customers follow a Markovian arrival process and they can be queued in an infinite buffer. The service time of a consumer customer follows a phase-type distribution. The system receives negative customers which have Poisson flows and as soon as a negative customer comes into the system, he causes a consumer customer to leave the system, if any. One of two inventory policies is used in the systems: either $(s,S)$ or $(s,Q)$. If the inventory level is zero when a consumer customer arrives, then this customer is either lost (lost sale) or joins the queue (backorder sale). The system is formulated by a four-dimensional continuous-time Markov chain. Ergodicity condition for both systems is established and steady-state distribution is obtained using the matrix-geometric method. By numerical studies, the influence of the distributions of the arrival process and the service time and the system parameters on performance measures are deeply analyzed. Finally, an optimization study is presented in which the criterion is the minimization of expected total costs and the controlled parameter is warehouse capacity. Full article

Background: Elucidating the clinical features of metastatic breast cancer (MBC) patients with an exceptionally favorable prognosis may offer insights to improve the survival of more typical patients. Methods: We collected comprehensive real-world data on clinicopathologic characteristics, treatments, and outcomes of 110 consecutive MBC patients who survived for over ten years from the clinical data warehouse of Samsung Medical Center. Results: The cohort included 54 hormone receptor (HR)-positive/HER2-negative (HR+/HER2−), 21 HR+/HER2+, 16 HR−/HER2+, and 14 triple-negative breast cancer (TNBC) patients. The median age at MBC diagnosis was 48.5 years. Approximately 70% of patients initially had a single-organ metastasis. The most common site of metastasis was the lung (46.4%), followed by distant lymph nodes (37.3%). During a median follow-up of 14.6 years, the median duration of systemic therapy was 11, 8.4, 7.3, and 0.8 years in the HR+/HER2−, HR+/HER2+, HR−/HER2+, and TNBC subgroups, respectively. Seven HER2+ and ten TNBC patients received systemic treatment for less than two years and remained treatment-free for most of the follow-up period, suggesting a potential chance of cure. The TNBC subtype (p < 0.001) and local treatment with curative intent within 1 year of MBC diagnosis (p = 0.002) were significantly associated with long-term treatment-free survival. The survival of HER2+ MBC and TNBC patients, but not that of HR+/HER2− patients, plateaued approximately 13 years after MBC diagnosis. Conclusions: A small subset of patients with HER2+ MBC and metastatic TNBC may be curable with multimodality therapy. Prospective studies integrating clinical and genomic data may identify unique clinicogenomic features of MBC patients who can achieve durable disease control without prolonged chemotherapy. Full article