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Introduction: Lung cancer is one of the most prevalent malignancies worldwide. Substantial research has illuminated the intricate interplay between microorganisms and human health, revealing their role in disease regulation. Trichomonads is a flagellated protozoan in the human cavity and have been previously identified as a pathogen associated with pneumonia, contributing to tissue chronic inflammation and carcinogenesis. Methods: Nested polymerase chain reaction methods were employed to scrutinize the prevalence of trichomonads in the bronchovesicular fluid of patients diagnosed with lung cancer. Subsequently, the influence of Trichomonas tenax invasion on lung cancer cells was elucidated through proliferation assays, migration assays, and transcription analysis. Results: Bronchoalveolar fluid samples from lung cancer patients yielded positive nested PCR results for eight out of twenty-seven samples. Seven of these samples were identified as Trichomonas tenax, while one was identified as Tetratrichomonas spp. Our findings revealed a significant upregulation of pathways associated with carcinogenesis, including cellular proliferation, migration, and drug resistance, in response to T. tenax invasion. Conclusions: This study underscores the importance of recognizing the presence of trichomonads and the influence of T. tenax invasion on host responses to respiratory diseases. The identified pathways implicated in cancer development may pave the way for developing targeted treatment strategies for pulmonary diseases. These findings hold promise for informing and improving the precision of therapeutic interventions in the context of pulmonary ailments.

Background: Microbiota manipulation through preoperative probiotic supplementation to reduce surgical site infection has shown promising results in recent years. However, the changes in gut microbiota resulting from this manipulation remain unclear. In eastern Asia, Clostridium butyricum (CBM588) has been demonstrated to have antimicrobial ability. This study aimed to explore postoperative outcomes associated with microbiota changes in a murine colonic anastomosis model. Materials and Methods: C57BL/6 mice were divided into four groups. All mice, including group A as the control, received standard oral neomycin and metronidazole before surgery. Group B received CBM588 before surgery. Group C received the antibiotic cocktail therapy before surgery. Group D received the antibiotic cocktail therapy followed by CBM588 supplementation before surgery. A 16S rRNA amplicon with SMS was used to show the impact of different treatments on the murine gut microbiota. Results: The implementation of the antibiotic cocktail therapy resulted in improved postoperative performance, significantly better anastomotic healing scores, and reduced areas of anastomotic necrosis in both group C and D, but particularly prominently in group D. Metagenomic analysis revealed that group D was enriched with the order Clostridiales, the family Clostridiaceae, and the genus Clostridium, with low bacterial diversity and distinct bacterial composition. Conclusions: A preoperative antibiotic cocktail therapy followed by CBM588 supplementation modulates bacterial diversity and composition, resulting in an increased percentage of Clostridiaceae in a murine colonic anastomosis model. The associated outcomes include lower intra-abdominal adhesion scores, improved postoperative performance, improved anastomotic healing scores, and reduced areas of anastomotic necrosis.

Trichomonas tenax, an oral commensal parasite commonly found in the human mouth, is associated with periodontitis and poor oral hygiene. However, it has also been identified in the bronchoalveolar lavage fluid (BALF) of individuals with lung diseases. Notably, significant quantities of T. tenax have been isolated following bronchoscopy in cases of empyema and acute respiratory distress syndrome (ARDS). Furthermore, research has demonstrated its ability to induce inflammation in pulmonary epithelial cells. To comprehend the potential role of T. tenax in pneumonia, it is crucial to elucidate the relationship between the parasite and the disease. We investigated the clinical factors associated with T. tenax infection in patients with pneumonia. Employing nested polymerase chain reactions, we amplified nucleic acids from BALF and analyzed the relationships between T. tenax and various clinical factors. Our data revealed a significant association between T. tenax and bacterial infections, high pneumonia severity index (PSI) scores, nasogastric tube feeding, and pulmonary complications. Logistic regression analyses also showed strong associations between T. tenax and these clinical factors in pneumonia patients. These findings suggest that T. tenax infection in pneumonia is accompanied by bacterial infection and severe clinical manifestations.

Abstract Background Acanthamoeba castellanii is a free-living protist that feeds on diverse bacteria. A. castellanii has frequently been utilized in studies on microbial interactions. Grazing bacteria also exhibit diverse effects on the physiological characteristics of amoebae, such as their growth, encystation, and cytotoxicity. Since the composition of amoebae amino acids is closely related to cellular activities, it can indicate the overall responses of A. castellanii to various stimuli. Method A. castellanii was exposed to different culture conditions in low-nutrient medium with heat-killed DH5α to clarify their effects. A targeted metabolomic technique was utilized to evaluate the concentration of cellular amino acids. The amino acid composition and pathways were analyzed by two web-based tools: MetaboAnalyst and Pathview. Then, long-term exposure to A. castellanii was investigated through in silico and in vitro methods to elucidate the homeostasis of amino acids and the growth of A. castellanii. Results Under short-term exposure, all kinds of amino acids were enriched in all exposed groups. In contrast to the presence of heat-killed bacteria, the medium exhibited obvious effects on the amino acid composition of A. castellanii. After long-term exposure, the amino acid composition was more similar to that of the control group. A. castellanii may achieve amino acid homeostasis through pathways related to alanine, aspartate, citrulline, and serine. Discussion Under short-term exposure, compared to the presence of bacteria, the type of medium exerted a more powerful effect on the amino acid composition of the amoeba. Previous studies focused on the interaction of the amoeba and bacteria with effective secretion systems and effectors. This may have caused the effects of low-nutrient environments to be overlooked. Conclusion When A. castellanii was stimulated in the coculture system through various methods, such as the presence of bacteria and a low-nutrient environment, it accumulated intracellular amino acids within a short period. However, different stimulations correspond to different amino acid compositions. After long-term exposure, A. castellanii achieved an amino acid equilibrium by downregulating the biosynthesis of several amino acids.

ABSTRACT Microorganisms that are involved in microbial invasion keratitis infiltrate the cornea and cause eye pain. Despite the administration of antimicrobial treatment, patients with amoebic keratitis develop bacterial coinfections. Pseudomonas aeruginosa infection accounts for 50% of the reported bacterial coinfections in these patients. However, the role of pathogenic bacteria in amoeba-induced corneal damage remains unclear. In our current study, we conducted assays to examine cytopathic effects and observed increased corneal cell damage in the group with secondary P. aeruginosa infections. Imaging revealed that the presence of A. castellanii enhanced the accumulation of P. aeruginosa in areas of cell monolayer leakage. We demonstrated that treatment of P. aeruginosa with amoeba-soluble antigen resulted in higher adhesion ability, increased biofilm formation, and more severe corneal cell damage. Overall, this research significantly contributes to our understanding of the risk of P. aeruginosa coinfection in the progression of Acanthamoeba keratitis. IMPORTANCE At the National Cheng Kung University Hospital, numerous cases of amoebic keratitis had been identified with concurrent bacterial infections. Among these bacterial coinfections, Pseudomonas aeruginosa accounted for 50% of the reported cases. However, the impact of pathogenic bacteria on amoeba-induced corneal damage remains unclear. In our study, we successfully demonstrated that P. aeruginosa accumulated on the Acanthamoeba castellanii surface and caused more severe corneal damage. We also indicated that the exposure of P. aeruginosa to amoeba-soluble antigens enhanced its adhesion ability, promoted biofilm formation, and led to more severe corneal cell damage. These findings significantly contributed to our understanding of the risk associated with P. aeruginosa coinfection in the progression of amoeba keratitis.

Acanthamoeba, a widely distributed free-living amoeba found in various environments, is an opportunistic pathogen responsible for causing Acanthamoeba keratitis, a condition that may lead to blindness. However, identifying the pathogenicity of Acanthamoeba is challenging due to its complex life cycle, ability to adapt to different environments, variable virulence factors, and intricate interactions with the host immune system. Additionally, the development of an effective model for studying Acanthamoeba pathogenicity is limited, hindering a comprehensive understanding of the mechanisms underlying its virulence and host interactions. The aim of this study was to develop an ex vivo model for Acanthamoeba infection using porcine eyeballs and to evaluate the pathogenicity of the Acanthamoeba isolates. Based on slit lamp and biopsy analysis, the developed ex vivo model is capable of successfully infecting Acanthamoeba within 3 days. Histopathological staining revealed that clinical isolates of Acanthamoeba exhibited greater corneal stroma destruction and invasion in this model than environmental isolates. Our results highlight the importance of an ex vivo porcine eye model in elucidating the pathogenesis of Acanthamoeba infection and its potential implications for understanding and managing Acanthamoeba-related ocular diseases.

Few studies have reported weight gain in patients with hepatitis C virus (HCV) infection treated with direct-acting antiviral agents (DAAs). This retrospective cohort study identified factors associated with substantial weight gain after DAA treatment in Taiwan. This study involved patients treated using DAAs at the Chiayi and Yunlin branches of Chang Gung Memorial Hospital from 1 January 2017 to 31 October 2020. Body weight data were collected at the start of DAA therapy and 2 years after the confirmation of a sustained virologic response. We performed multiple logistic regression to evaluate the clinical and laboratory parameters associated with a large body mass index (BMI) increase (≥5%). The mean BMI was 25.56 ± 4.07 kg/m2 at baseline and 25.77 ± 4.29 kg/m2 at the endpoint (p = 0.005). A considerable reduction in fibrosis-4 (FIB-4) score was a significant predictor of a large BMI increase (OR: 1.168; 95% CI: 1.047–1.304, p = 0.006). By contrast, older age (OR: 0.979; 95% CI: 0.963–0.996, p = 0.013) and a higher baseline BMI (OR: 0.907; 95% CI: 0.863–0.954, p < 0.001) were associated with a reduced risk of a large increase in BMI at the endpoint. In summary, a larger BMI increase was closely associated with a younger age, lower baseline BMI, and higher FIB-4 score reduction. Notably, differences in DAA regimens did not affect outcomes. Future studies are needed to elucidate the long-term effects and metabolic outcomes associated with this body weight change and investigate the exact underlying mechanisms.

The dispatching of automated guided vehicles (AGVs) is essential for efficient horizontal transportation at automated container terminals. Effective planning of AGV transportation can reduce equipment energy consumption and shorten task completion time. Multiple AGVs transport containers between storage blocks and vessels, which can be regarded as the supply sides and demand points of containers. To meet the requirements of shipment in terms of timely and high-efficient delivery, multiple AGVs should be dispatched to deliver containers, which includes assigning tasks and selecting paths. A contract net protocol (CNP) is employed for task assignment in a multiagent system, while machine learning provides a logical alternative, such as Q-learning (QL), for complex path planning. In this study, mathematical models for multi-AGV dispatching are established, and a QL-CNP algorithm is proposed to tackle the multi-AGV dispatching problem (MADP). The distribution of traffic load is balanced for multiple AGVs performing tasks in the road network. The proposed model is validated using a Gurobi solver with a small experiment. Then, QL-CNP is used to conduct experiments with different sizes. The other algorithms, including Dijkstra, GA, and PSO, are also compared with the QL-CNP algorithm. The experimental results demonstrate the superiority of the proposed QL-CNP when addressing the MADP.

Summary: Membrane technology has gained tremendous attention for removing pollutants from wastewater, mainly due to their affordable capital cost, miniature equipment size, low energy consumption, and high efficiency even for the pollutants present in lower concentrations. In this paper, we review the literature to summarize the progress of nanomaterial-modified membranes for wastewater treatment applications. Introduction of nanomaterial in the polymeric matrix influences membrane properties such as surface roughness, hydrophobicity, porosity, and fouling resistance. This review also covers the importance of functionalization strategies to prepare thin-film nanocomposite hybrid membranes and their effect on eliminating pollutants. Systematic discussion regarding the impact of the nanomaterials incorporated within membrane, toward the recovery of various pollutants such as metal ions, organic compounds, dyes, and microbes. Successful examples are provided to show the potential of nanomaterial-functionalized membranes for regeneration of wastewater. In the end, future prospects are discussed to develop nanomaterial-based membrane technology.

Abstract Clinical trials showed pangenotypic direct-acting antivirals’ (DAAs) excellent efficacy and safety when treating hepatitis C virus (HCV). Two pangenotypic regimens were examined, glecaprevir/pibrentasvir (GLE/PIB) and sofosbuvir/velpatasvir (SOF/VEL), in a real-world Taiwanese setting, including all HCV patients treated with GLE/PIB or SOF/VEL from August 2018 to April 2020. The primary endpoint was sustained virologic response 12 weeks after treatment cessation (SVR12), including adverse events (AEs). A total of 1,356 HCV patients received pangenotypic DAA treatment during the study: 742 and 614 received GLE/PIB and SOF/VEL, respectively. The rates of SVR12 for GLE/PIB and SOF/VEL were 710/718 (98.9%) and 581/584 (99.5%), respectively, by per-protocol analysis, and 710/742 (95.7%) and 581/614 (94.6%), respectively, by evaluable population analysis. Eleven (GLE/PIB: 8, SOF/VEL: 3) did not achieve SVR12. The most common AEs for GLE/PIB and SOF/VEL were pruritus (17.4% vs. 2.9%), abdominal discomfort (5.8% vs. 4.4%), dizziness (4.2% vs. 2%), and malaise (3.1% vs. 2.9%). Laboratory abnormalities were uncommon; only

Blastocystis hominis is an intestinal protozoan that is often neglected, despite causing abdominal pain and diarrhea. Previous research has demonstrated that lipids can be synthesized by B. hominis or can accumulate in growth medium, but their function and mechanisms in the pathogenesis of Blastocystis remain unclear. Our study found that lipid-rich Blastocystis ST7-B can increase inflammation and disrupt Caco-2 cells more than the same parasite without the lipovenoes supplement. Additionally, the cysteine protease of Blastocystis, a virulence factor, is upregulated and has higher activity in lipid-rich Blastocystis. In order to better understand the effects of lipids on Blastocystis pathogenesis, we treated lipid-lowering pravastatin during Blastocystis ST7-B culturing with a lipovenoes supplement, which decreased the lipid levels of the Blastocystis and reduced the Blastocystis-induced inflammation and cell disruption of Caco-2 cells. We also analyzed the fatty acid composition and possible synthesis pathway in Blastocystis ST7-B, finding significantly higher ratios of arachidonic acid, oleic acid, and palmitic acid than in the other lipid components in lipid-rich Blastocystis ST7-B. These results suggest that lipids play a significant role in the pathogenesis of Blastocystis and provide important information on the molecular mechanisms of and potential treatments for Blastocystis infection.

ABSTRACT BACKGROUND: The COVID-19 pandemic has instilled fear and stress among healthcare workers. OBJECTIVES: The aim of this study was to assess work stress and associated factors among healthcare workers during the COVID-19 outbreak and to evaluate whether prior experience of treating severe acute respiratory syndrome (SARS) had a positive or negative influence on healthcare workers’ stress levels during the COVID-19 pandemic. DESIGN AND SETTING: Cross-sectional survey in a tertiary hospital in Kaohsiung City, in southern Taiwan. METHODS: The survey was conducted using an online self-administered questionnaire to measure the stress levels among healthcare workers from March 20 to April 20, 2020. The stress scales were divided into four subscales: worry of social isolation; discomfort caused by the protective equipment; difficulties and anxiety regarding infection control; and workload of caring for patients. RESULTS: The total stress scores were significantly higher among healthcare workers who were aged 41 or above, female, married, parents and nurses. Those with experience of treating SARS reported having significantly higher stress scores on the subscale measuring the discomfort caused by protective equipment and the workload of caring for patients. During the COVID-19 outbreak, frontline healthcare workers with experience of treating SARS indicated having higher stress levels regarding the workload of caring for patients than did non-frontline healthcare workers with no experience of treating SARS. CONCLUSIONS: Work experience from dealing with the 2003 SARS virus may have had a negative psychological impact on healthcare workers amidst the COVID-19 outbreak.

Abstract This study aimed to investigate the perceived work stress and its influencing factors among hospital staff during the novel coronavirus (COVID‐19) pandemic in Taiwan. A web‐based survey was conducted at one medical center and two regional hospitals in southern Taiwan, targeting physicians, nurses, medical examiners, and administrators. The questionnaire included items on the demographic characteristics of hospital staff and a scale to assess stress among healthcare workers caring for patients with a highly infectious disease. A total of 752 valid questionnaires were collected. The hospital staff reported a moderate level of stress and nurses had a highest level of stress compared to staff in the other three occupational categories. The five highest stress scores were observed for the items “rough and cracked hands due to frequent hand washing and disinfectant use,” “inconvenience in using the toilet at work,” “restrictions on eating and drinking at work,” “fear of transmitting the disease to relatives and friends,” and “fear of being infected with COVID‐19.” Discomfort caused by protective equipment was the major stressor for the participants, followed by burden of caring for patients. Among participants who experienced severe stress (n = 129), work stress was higher among those with rather than without minor children. The present findings may serve as a reference for future monitoring of hospital staff's workload, and may aid the provision of support and interventions.

BackgroundThe use of artificial intelligence (AI) in the medical domain has attracted considerable research interest. Inference applications in the medical domain require energy-efficient AI models. In contrast to other types of data in visual AI, data from medical laboratories usually comprise features with strong signals. Numerous energy optimization techniques have been developed to relieve the burden on the hardware required to deploy a complex learning model. However, the energy efficiency levels of different AI models used for medical applications have not been studied. ObjectiveThe aim of this study was to explore and compare the energy efficiency levels of commonly used machine learning algorithms—logistic regression (LR), k-nearest neighbor, support vector machine, random forest (RF), and extreme gradient boosting (XGB) algorithms, as well as four different variants of neural network (NN) algorithms—when applied to clinical laboratory datasets. MethodsWe applied the aforementioned algorithms to two distinct clinical laboratory data sets: a mass spectrometry data set regarding Staphylococcus aureus for predicting methicillin resistance (3338 cases; 268 features) and a urinalysis data set for predicting Trichomonas vaginalis infection (839,164 cases; 9 features). We compared the performance of the nine inference algorithms in terms of accuracy, area under the receiver operating characteristic curve (AUROC), time consumption, and power consumption. The time and power consumption levels were determined using performance counter data from Intel Power Gadget 3.5. ResultsThe experimental results indicated that the RF and XGB algorithms achieved the two highest AUROC values for both data sets (84.7% and 83.9%, respectively, for the mass spectrometry data set; 91.1% and 91.4%, respectively, for the urinalysis data set). The XGB and LR algorithms exhibited the shortest inference time for both data sets (0.47 milliseconds for both in the mass spectrometry data set; 0.39 and 0.47 milliseconds, respectively, for the urinalysis data set). Compared with the RF algorithm, the XGB and LR algorithms exhibited a 45% and 53%-60% reduction in inference time for the mass spectrometry and urinalysis data sets, respectively. In terms of energy efficiency, the XGB algorithm exhibited the lowest power consumption for the mass spectrometry data set (9.42 Watts) and the LR algorithm exhibited the lowest power consumption for the urinalysis data set (9.98 Watts). Compared with a five-hidden-layer NN, the XGB and LR algorithms achieved 16%-24% and 9%-13% lower power consumption levels for the mass spectrometry and urinalysis data sets, respectively. In all experiments, the XGB algorithm exhibited the best performance in terms of accuracy, run time, and energy efficiency. ConclusionsThe XGB algorithm achieved balanced performance levels in terms of AUROC, run time, and energy efficiency for the two clinical laboratory data sets. Considering the energy constraints in real-world scenarios, the XGB algorithm is ideal for medical AI applications.

Real-world data on the effectiveness of glecaprevir/pibrentasvir (GLE/PIB) for patients with HCV infection and compensated cirrhosis is limited, especially for the 8-week regimen and in an Asian population. This retrospective study enrolled 159 consecutive patients with HCV and compensated cirrhosis who were treated with GLE/PIB at a single center in Taiwan. Sustained virological response (SVR) and adverse events (AEs) were evaluated. Among the 159 patients, 91 and 68 were treated with GLE/PIB for 8 and 12 weeks, respectively. In the per protocol analysis, both the 8- and 12-week groups achieved 100% SVR (87/87 vs. 64/64); and in the evaluable population analysis, 95.6% (87/91) of the 8-week group and 94.1% (64/68) of the 12-week group achieved SVR. The most commonly reported AEs, which included pruritus (15.4% vs. 26.5%), abdominal discomfort (9.9% vs. 5.9%), and skin rash (5.5% vs. 5.9%), were mild for the 8- and 12-week groups. Two patients in the 8-week group exhibited total bilirubin elevation over three times the upper normal limit. One of these two patients discontinued GLE/PIB treatment after 2 weeks but still achieved SVR. Both 8- and 12-week GLE/PIB treatments are safe and effective for patients of Taiwanese ethnicity with HCV and compensated cirrhosis.

The findings regarding changes in renal function in patients with hepatitis C virus (HCV) infection treated with direct-acting antivirals (DAAs) are controversial. This study attempted to identify the factors associated with the large decline in renal function following DAA treatment. This retrospective cohort study included patients treated with DAAs at Chiayi and Yunlin Chang Gung Hospitals, Taiwan, from 1 January 2017 to 31 October 2020. Estimated glomerular filtration rate (eGFR) data were collected within 90 days prior to DAA therapy and 2 years after the confirmation of a sustained virologic response (SVR). We performed multiple logistic regression to evaluate the clinical or laboratory parameters associated with a large eGFR decline (≥10%). Among the enrolled 606 patients, the mean eGFR at the baseline and endpoint were 84.11 ± 24.38 and 78.88 ± 26.30 mL/min/1.73 m2, respectively (p < 0.001). The factors associated with a large eGFR decline 2 years after the SVR included hypertension (OR: 1.481; 95% CI: 1.010–2.173, p = 0.044) and a higher baseline eGFR (OR: 1.016; 95% CI: 1.007–1.024, p < 0.001). A higher albumin level reduced the risk of a large eGFR decline (OR: 0.546; 95% CI: 0.342–0.872, p = 0.011). In the patients with HCV treated with DAAs, a larger renal function decline was more commonly observed in those with hypertension, a lower (but within normal range) albumin level, and a higher baseline eGFR, while DAA treatment had no effect. The clinical significance of these findings has to be further defined. Although some risk factors associated with chronic kidney disease may be alleviated after DAA treatment, the regular control and follow-up of risk factors and renal function are still recommended in at-risk patients after HCV eradication.

As antibiotics resistance on superbugs has risen, more and more studies have focused on developing rapid antibiotics susceptibility tests (AST). Meanwhile, identification of multiple antibiotics resistance on Staphylococcus aureus provides instant information which can assist clinicians in administrating the appropriate prescriptions. In recent years, matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has emerged as a powerful tool in clinical microbiology laboratories for the rapid identification of bacterial species. Yet, lack of study devoted on providing efficient methods to deal with the MS shifting problem, not to mention to providing tools incorporating the MALDI-TOF MS for the clinical use which deliver the instant administration of antibiotics to the clinicians. In this study, we developed a web tool, MDRSA, for the rapid identification of oxacillin-, clindamycin-, and erythromycin-resistant Staphylococcus aureus. Specifically, the kernel density estimation (KDE) was adopted to deal with the peak shifting problem, which is critical to analyze mass spectra data, and machine learning methods, including decision trees, random forests, and support vector machines, which were used to construct the classifiers to identify the antibiotic resistance. The areas under the receiver operating the characteristic curve attained 0.8 on the internal (10-fold cross validation) and external (independent testing) validation. The promising results can provide more confidence to apply these prediction models in the real world. Briefly, this study provides a web-based tool to provide rapid predictions for the resistance of antibiotics on Staphylococcus aureus based on the MALDI-TOF MS data. The web tool is available at: http://fdblab.csie.ncu.edu.tw/mdrsa/.

ABSTRACT Acanthamoeba castellanii is a free-living, pathogenic ameba found in the soil and water. It invades the body through ulcerated skin, the nasal passages, and eyes and can cause blinding keratitis and granulomatous encephalitis. However, the mechanisms underlying the opportunistic pathogenesis of A. castellanii remain unclear. In this study, we observed that commensal bacteria significantly reduced the cytotoxicity of the ameba on mammalian cells. This effect occurred in the presence of both Gram-positive and Gram-negative commensals. Additionally, commensals mitigated the disruption of cell junctions. Ex vivo experiments on mouse eyeballs further showed that the commensals protected the corneal epithelial layer. Together, these findings indicate that A. castellanii is pathogenic to individuals with a dysbiosis of the microbiota at infection sites, further highlighting the role of commensals as a natural barrier during parasite invasion. IMPORTANCE Acanthamoeba castellanii, an opportunistic protozoan widely present in the environment, can cause Acanthamoeba keratitis and encephalitis in humans. However, only a few reports describe how the ameba acts as an opportunistic pathogen. Our study showed that the normal microbiota interfered with the cytotoxicity of Acanthamoeba, persevered during Acanthamoeba invasion, and reduced corneal epithelium peeling in the mouse eyeball model. This suggests that commensals may act as a natural barrier against Acanthamoeba invasion. In future, individuals who suffer from Acanthamoeba keratitis should be examined for microbiota absence or dysbiosis to reduce the incidence of Acanthamoeba infection in clinical settings.

Abstract Trichomonas vaginalis (T. vaginalis) detection remains an unsolved problem in using of automated instruments for urinalysis. The study proposes a machine learning (ML)-based strategy to increase the detection rate of T. vaginalis in urine. On the basis of urinalysis data from a teaching hospital during 2009–2013, individuals underwent at least one urinalysis test were included. Logistic regression, support vector machine, and random forest, were used to select specimens with a high risk of T. vaginalis infection for confirmation through microscopic examinations. A total of 410,952 and 428,203 specimens from men and women were tested, of which 91 (0.02%) and 517 (0.12%) T. vaginalis-positive specimens were reported, respectively. The prediction models of T. vaginalis infection attained an area under the receiver operating characteristic curve of more than 0.87 for women and 0.83 for men. The Lift values of the top 5% risky specimens were above eight. While the most risky vigintile was picked out by the models and confirmed by microscopic examination, the incremental cost-effectiveness ratios for T. vaginalis detection in men and women were USD$170.1 and USD$29.7, respectively. On the basis of urinalysis, the proposed strategy can significantly increase the detection rate of T. vaginalis in a cost-effective manner.

The third wave of information technology (IT) competition has enabled one promising value co-creation proposition, Smart PSS (smart product-service systems). Manufacturing companies offer smart, connected products with various e-services as a solution bundle to meet individual customer satisfaction, and in return, collect and analyze usage data for evergreen design purposes in a circular manner. Despite a few works discussing such value co-creation business mechanism, scarcely any has been reported from technical aspect to realizing this data-driven manufacturer/service provider-customer interaction cost-effectively. To fill this gap, a novel hybrid crowd sensing approach is proposed, and adopted in the Smart PSS context. It leverages large-scale mobile devices and their massive user-generated/product-sensed data, and converges with reliable static sensing nodes and other data sources in the smart, connected environment for value generation. Both the proposed hybrid crowd sensing conceptual framework and its systematic information modeling process are introduced. An illustrative example of smart water dispenser maintenance service design is given to validate its feasibility. The result shows that the proposed approach can be a promising manner to enable value co-creation process cost-effectively.

The concept of the sharing economy has attracted wide attention due to its huge impact on transforming traditional industries. Online car-hailing, combining the sharing economy and ICT technologies, shapes a new landscape, which can greatly shorten the traveler's waiting time and reduce the empty-run rate of cars (such as Uber and DiDi). However, a few comprehensive studies have been conducted on the sustainable development of online car-hailing considering both the user experience and the operational cost. To address this issue, this paper systematically studies the influencing factors, their relations, and their impacts on online car-hailing in an empirical way. First, an index system in four key aspects, namely service, price, safety, and traveling time, is established to evaluate the user experience. Second, the Bayesian network theory is employed to model the complexity of each factor and the extent of its influence on the online car-hailing system with expert scores. The two most important influence paths affecting the passengers' choices of online car-hailing are determined. Third, we further construct an investment allocation model with the aim of minimizing the economic cost of the online car-hailing system while maintaining the system performance, considering the limiting factors, such as the complexity and cost. Finally, we perform a simulation experiment, which generates some practical suggestions for improving the online car-hailing system.

Current guidelines recommend antibiotic prophylaxis for all patients with various degrees of cirrhosis and upper gastrointestinal (UGI) bleeding. This study assessed the need for antibiotic prophylaxis in patients with low Child-Pugh scores. We retrospectively screened all patients with cirrhosis who underwent upper endoscopies for UGI bleeding in a referral hospital in Taiwan between 2003 and 2014, from which 913 patients were enrolled after excluding patients with active bacterial infections, recent antibiotic use, early death, and Child-Pugh class C cirrhosis. Among them, 73 (8%) received prophylactic antibiotics, and 45 (4.9%) exhibited 14-day bacterial infection. Neither Child-Pugh score nor model for end stage liver disease score were optimal for predicting bacterial infection because their areas under the curves were 0.610 (95% confidence interval [CI]: 0.529-0.691) and 0.666 (95% CI: 0.591-0.742), respectively. Antibiotic prophylaxis did not reduce the risks of 14-day bacterial infection (relative risk [RR]: 0.932, 95% CI: 0.300-2.891, P = 0.902), 14-day rebleeding (RR: 0.791, 95% CI: 0.287-2.181, P = 0.650), or 42-day mortality (RR: 2.710, 95% CI: 0.769-9.524, P = 0.121). The results remained similar after propensity score adjustment. On-demand antibiotic treatment might suffice for patients with Child-Pugh class A/B cirrhosis and UGI bleeding.

Cordycepin is an adenosine derivative isolated from Cordyceps sinensis, which has been used as an herbal complementary and alternative medicine with various biological activities. The general anti-cancer mechanisms of cordycepin are regulated by the adenosine A3 receptor, epidermal growth factor receptor (EGFR), mitogen-activated protein kinases (MAPKs), and glycogen synthase kinase (GSK)-3β, leading to cell cycle arrest or apoptosis. Notably, cordycepin also induces autophagy to trigger cell death, inhibits tumor metastasis, and modulates the immune system. Since the dysregulation of autophagy is associated with cancers and neuron, immune, and kidney diseases, cordycepin is considered an alternative treatment because of the involvement of cordycepin in autophagic signaling. However, the profound mechanism of autophagy induction by cordycepin has never been reviewed in detail. Therefore, in this article, we reviewed the anti-cancer and health-promoting effects of cordycepin in the neurons, kidneys, and the immune system through diverse mechanisms, including autophagy induction. We also suggest that formulation changes for cordycepin could enhance its bioactivity and bioavailability and lower its toxicity for future applications. A comprehensive understanding of the autophagy mechanism would provide novel mechanistic insight into the anti-cancer and health-promoting effects of cordycepin.

Staphylococcus haemolyticus is one of the most significant coagulase-negative staphylococci, and it often causes severe infections. Rapid strain typing of pathogenic S. haemolyticus is indispensable in modern public health infectious disease control, facilitating the identification of the origin of infections to prevent further infectious outbreak. Rapid identification enables the effective control of pathogenic infections, which is tremendously beneficial to critically ill patients. However, the existing strain typing methods, such as multi-locus sequencing, are of relatively high cost and comparatively time-consuming. A practical method for the rapid strain typing of pathogens, suitable for routine use in clinics and hospitals, is still not available. Matrix-assisted laser desorption ionization-time of flight mass spectrometry combined with machine learning approaches is a promising method to carry out rapid strain typing. In this study, we developed a statistical test-based method to determine the reference spectrum when dealing with alignment of mass spectra datasets, and constructed machine learning-based classifiers for categorizing different strains of S. haemolyticus. The area under the receiver operating characteristic curve and accuracy of multi-class predictions were 0.848 and 0.866, respectively. Additionally, we employed a variety of statistical tests and feature-selection strategies to identify the discriminative peaks that can substantially contribute to strain typing. This study not only incorporates statistical test-based methods to manage the alignment of mass spectra datasets but also provides a practical means to accomplish rapid strain typing of S. haemolyticus.

Branded apps are not only an important platform for enterprises and customers to have real-time interactions and communicate marketing messages, but also a new business model that encourages value co-creation between the two. In order to explore the impact of branded apps on customers, this study constructs a fuzzy multi-criteria decision making (FMCDM) analysis model, and it uses consistent fuzzy linguistic preference relations (CFLPR) to set up a symmetric pairwise comparison matrix, which greatly reduces the complexity and error rate of calculations. Empirical research findings show that brand experience attributes and the influence of brand experience on customer loyalty and satisfaction can be more accurately measured. As a consequence of this study, we show that, among the brand experience facets of two retail chain branded apps, behavioral experience is the most favored, while affective experience is the least favored. Furthermore, brand attachment and active participation should be strengthened to enhance customer loyalty. Through the analytical model employed in this study, enterprises can regularly monitor changes in the brand experience preferences of branded app users and evaluate app performance to flexibly adjust mobile device-based marketing campaigns and strategies. It can also aid enterprises in using mobile devices effectively to improve customer loyalty and address the issue of diminishing brand loyalty.

Background The accurate and rapid preliminarily identification of the types of methicillin-resistant Staphylococcus aureus (MRSA) is crucial for infection control. Currently, however, expensive, time-consuming, and labor-intensive methods are used for MRSA typing. By contrast, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) is a potential tool for preliminary lineage typing. The approach has not been standardized, and its performance has not been analyzed in some regions with geographic barriers (e.g., Taiwan Island). Methods The mass spectra of 306 MRSA isolates were obtained from multiple reference hospitals in Taiwan. The multilocus sequence types (MLST) of the isolates were determined. The spectra were analyzed for the selection of characteristic peaks by using the ClinProTools software. Furthermore, various machine learning (ML) algorithms were used to generate binary and multiclass models for classifying the major MLST types (ST5, ST59, and ST239) of MRSA. Results A total of 10 peaks with the highest discriminatory power (m/z range: 2,082–6,594) were identified and evaluated. All the single peaks revealed significant discriminatory power during MLST typing. Moreover, the binary and multiclass ML models achieved sufficient accuracy (82.80–94.40% for binary models and >81.00% for multiclass models) in classifying the major MLST types. Conclusions A combination of MALDI-TOF MS analysis and ML models is a potentially accurate, objective, and efficient tool for infection control and outbreak investigation.

Heterogeneous vancomycin-intermediate Staphylococcus aureus (hVISA) is an emerging superbug with implicit drug resistance to vancomycin. Detecting hVISA can guide the correct administration of antibiotics. However, hVISA cannot be detected in most clinical microbiology laboratories because the required diagnostic tools are either expensive, time consuming, or labor intensive. By contrast, matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) is a cost-effective and rapid tool that has potential for providing antibiotics resistance information. To analyze complex MALDI-TOF mass spectra, machine learning (ML) algorithms can be used to generate robust hVISA detection models. In this study, MALDI-TOF mass spectra were obtained from 35 hVISA/vancomycin-intermediate S. aureus (VISA) and 90 vancomycin-susceptible S. aureus isolates. The vancomycin susceptibility of the isolates was determined using an Etest and modified population analysis profile–area under the curve. ML algorithms, namely a decision tree, k-nearest neighbors, random forest, and a support vector machine (SVM), were trained and validated using nested cross-validation to provide unbiased validation results. The area under the curve of the models ranged from 0.67 to 0.79, and the SVM-derived model outperformed those of the other algorithms. The peaks at m/z 1132, 2895, 3176, and 6591 were noted as informative peaks for detecting hVISA/VISA. We demonstrated that hVISA/VISA could be detected by analyzing MALDI-TOF mass spectra using ML. Moreover, the results are particularly robust due to a strict validation method. The ML models in this study can provide rapid and accurate reports regarding hVISA/VISA and thus guide the correct administration of antibiotics in treatment of S. aureus infection.

Methicillin-resistant Staphylococcus aureus (MRSA), one of the most important clinical pathogens, conducts an increasing number of morbidity and mortality in the world. Rapid and accurate strain typing of bacteria would facilitate epidemiological investigation and infection control in near real time. Matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry is a rapid and cost-effective tool for presumptive strain typing. To develop robust method for strain typing based on MALDI-TOF spectrum, machine learning (ML) is a promising algorithm for the construction of predictive model. In this study, a strategy of building templates of specific types was used to facilitate generating predictive models of methicillin-resistant Staphylococcus aureus (MRSA) strain typing through various ML methods. The strain types of the isolates were determined through multilocus sequence typing (MLST). The area under the receiver operating characteristic curve (AUC) and the predictive accuracy of the models were compared. ST5, ST59, and ST239 were the major MLST types, and ST45 was the minor type. For binary classification, the AUC values of various ML methods ranged from 0.76 to 0.99 for ST5, ST59, and ST239 types. In multiclass classification, the predictive accuracy of all generated models was more than 0.83. This study has demonstrated that ML methods can serve as a cost-effective and promising tool that provides preliminary strain typing information about major MRSA lineages on the basis of MALDI-TOF spectra.

In the Industry 4.0 environment, the new manufacturing transformation of mass customization for high-complexity and low-volume production is moving forward. Based on cyber-physical system (CPS) and Internet of things (IoT) technology, the flexible transformation of the manufacturing process to suit diverse customer manufacturing requirements is very possible, with the potential to provide digital “make-to-order” (MTO) services with a quick response time. To achieve this potential, a product configuration system, which translates the voice of customers to technical specifications, is needed. The purpose of this study is to propose a methodology for developing a quick-response product configuration system to enhance the communication between the customer and the manufacturer. The aim is to find an approach to receive requests from customers as inputs and generate a product configuration as outputs that maximizes customer satisfaction. In this approach, engineering characteristics (ECs) are defined, and selection pools are initially constructed. Then, quality function deployment (QFD) is modified and integrated with the Kano model to qualitatively and quantitatively analyze the relationship between customer requirements (CRs) and customer satisfaction (CS). Next, a mathematical programming model is applied to maximize the overall customer satisfaction level and recommend an optimal product configuration. Finally, sensitivity analysis is conducted to suggest revisions for customers and determine the final customized product specification. A case study and an OrderAssistant system are implemented to demonstrate the procedure and effectiveness of the proposed quick response product configuration system.

Analytic measurement of serum tumour markers is one of commonly used methods for cancer risk management in certain areas of the world (e.g. Taiwan). Recently, cancer screening based on multiple serum tumour markers has been frequently discussed. However, the risk-benefit outcomes appear to be unfavourable for patients because of the low sensitivity and specificity. In this study, cancer screening models based on multiple serum tumour markers were designed using machine learning methods, namely support vector machine (SVM), k-nearest neighbour (KNN), and logistic regression, to improve the screening performance for multiple cancers in a large asymptomatic population.AFP, CEA, CA19-9, CYFRA21-1, and SCC were determined for 20 696 eligible individuals. PSA was measured in men and CA15-3 and CA125 in women. A variable selection process was applied to select robust variables from these serum tumour markers to design cancer detection models. The sensitivity, specificity, positive predictive value (PPV), negative predictive value, area under the curve, and Youden index of the models based on single tumour markers, combined test, and machine learning methods were compared. Moreover, relative risk reduction, absolute risk reduction (ARR), and absolute risk increase (ARI) were evaluated.To design cancer detection models using machine learning methods, CYFRA21-1 and SCC were selected for women, and all tumour markers were selected for men. SVM and KNN models significantly outperformed the single tumour markers and the combined test for men. All 3 studied machine learning methods outperformed single tumour markers and the combined test for women. For either men or women, the ARRs were between 0.003-0.008; the ARIs were between 0.119-0.306.Machine learning methods outperformed the combined test in analysing multiple tumour markers for cancer detection. However, cancer screening based solely on the application of multiple tumour markers remains unfavourable because of the inadequate PPV, ARR, and ARI, even when machine learning methods were incorporated into the analysis.

Alzheimer's disease (AD) is a chronic and progressive neurodegenerative disorder that affects millions of individuals worldwide. Researchers have conducted numerous studies to find accurate biomarkers for early AD diagnosis and develop more effective treatments. The main pathological hallmarks of AD are amyloid beta and Tau proteins. Other biomarkers, such as DNA, RNA, and proteins, can also be helpful in early AD diagnosis. To diagnose and treat AD promptly, it is essential to accurately measure the concentration of biomarkers in the cerebrospinal fluid or blood. However, due to the low concentrations of these biomarkers in the body, highly sensitive analytical techniques are required. To date, sensors have become increasingly important due to their high sensitivity, swift detection, and adaptable manipulation features. These qualities make them an excellent substitute for conventional instruments. Nanomaterials are commonly employed in sensors to amplify signals and improve sensitivity. This review paper summarized the integration of nanomaterials in optical sensor systems, including colorimetric, fluorescent, and surface-enhanced Raman scattering sensors for AD biomarkers detection. [ABSTRACT FROM AUTHOR]