Bronchoscopic management of bronchopleural fistula using free fat pad transplant with platelet-rich plasma: a case study.

BACKGROUND: A bronchopleural fistula (BPF) occurs when an abnormal connection forms between the bronchial tubes and pleural cavity, often due to surgery, infection, trauma, radiation, or chemotherapy. The outcomes of both surgical and bronchoscopic treatments frequently prove to be unsatisfactory. CASE PRESENTATION: Here, we report a case of successful bronchoscopic free fat pad transplantation combined with platelet-rich plasma, effectively addressing a post-lobectomy BPF. Contrast-enhanced chest tomography revealed pleural thickening with heterogeneous consolidations over the right upper and middle lobes, indicative of destructive lung damage and bronchiectasis. The patient underwent thoracoscopic bilobectomy of the lungs. During surgery, severe adhesions and calcification of the chest wall and lung parenchyma were observed. The entire hilar structure was calcified, presenting challenges for dissection, despite the assistance of energy devices. Bronchoscopic intervention was required, during which two abdominal subcutaneous fat pads were retrieved. CONCLUSION: This innovative approach offers promise in the management of BPF and signals potential advancements in enhancing treatment efficacy and patient recovery.

Meta-analytic structural equation modeling for exploring workplace friendship, well-being, and organizational commitment.

BACKGROUND: Workplace friendships, which are informal relationships in the workplace, foster mutual trust, assistance, and emotional support and create a sense of belonging among coworkers. Employees with positive perceptions of their job responsibilities, psychological needs, and work environment experience happiness, satisfaction, and an overall sense of well-being. A heightened sense of well-being in the workplace positively affects employee satisfaction with job tasks and the work environment, reinforcing employees' sense of belonging and organizational commitment. OBJECTIVE: The study proposed and validated a theoretical model, with meta-analytic structural equation modeling employed to conduct quantitative, empirical research on workplace friendships, well-being, and organizational commitment in Taiwan and to analyze correlations between relevant variables. METHODS: The theoretical model was further validated through structural equation modeling, which yielded favorable goodness of fit in terms of various indicators. RESULTS: The mediating effect of well-being was verified through bootstrapping analysis. Within a 95% confidence interval, the total effect size of workplace friendships on organizational commitment was 0.549, which is the sum of its direct effect (0.255) and indirect effect (0.294). CONCLUSIONS: This finding indicates that well-being plays a mediating role in the association between workplace friendships and organizational commitment. The proposed theoretical model in this study is supported. Finally, the research results are discussed, and practical suggestions are provided.

Evaluation of spatial and temporal changes in illicit drug use in the Taipei metropolitan area via wastewater-based epidemiology.

Taiwan, identified as pivotal in the Asian drug trafficking chain, has been experiencing a surge in illicit drug-related issues. Wastewater-based epidemiology (WBE) has emerged as a promising approach for comprehensive evaluation of actual illicit drug usage. This study presents the first WBE investigation of illicit drug consumption in Taiwan based on the analysis of wastewater from four wastewater treatment plants (WWTPs) in the Taipei metropolitan area. Additionally, it demonstrates a high correlation between the amounts of illicit drugs seized and influent concentrations over an extended period of time. The reliability of solid-phase extraction and analysis via high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) was validated for 16 illicit drugs (methamphetamine, ketamine, cocaine, codeine, methadone, morphine, meperidine, fentanyl, sufentanil, para-methoxyamphetamine (PMA), para-methoxymethamphetamine (PMMA), 3,4-methylenedioxymethamphetamine (MDMA), cathinone, methcathinone, mephedrone (MEPH), and 4-methylethcathinone (4-MEC)). Methamphetamine, ketamine, and 4-MEC were consistently detected in all wastewater samples, underscoring their prevalence in the Taipei metropolitan area. Biochemical oxygen demand (BOD) and ammonia nitrogen (ammonia N) were employed to reduce uncertainty in estimations of population size during back-calculation of illicit drug consumption. The results indicate that methamphetamine was the most consumed drug (175-740 mg day-1 1000 people-1), followed by ketamine (22-280 mg day-1 1000 people-1). In addition, urban-related WWTPs exhibited higher consumption of methamphetamine and ketamine than did the suburban-related WWTP, indicating distinct illicit drug usage patterns between suburban and urban regions. Moreover, an examination of temporal trends in wastewater from the Dihua WWTP revealed a persistent predominance of ketamine and methamphetamine, consistent with statistical data pertaining to seizure quantities and urine test results. The study provides encouraging insight into spatial and temporal variations in illicit drug usage in the Taipei metropolitan area, emphasizing the complementary role of WBE in understanding trends in illicit drug abuse.

Solar-driven persulfate degradation of caffeine and cephradine in synthetic human urine.

Urine source separation, as an innovative concept for the reuse of microlevel nutrients in human urine, has drawn increasing attention recently. Consequently, removing coexisting pharmaceuticals in urine is necessary for further reuse. This study is the first to apply the solar-driven persulfate process (Solar/PS) to the investigation of cephradine (CFD) and caffeine (CAF) degradation in synthetic human urine. The results showed that significantly more degradation of CFD and CAF occurs with the Solar/PS process than with persulfate oxidation and direct sunlight photolysis, respectively. The generated reactive species ·OH, SO4·-, O2·- and 1O2 were identified in the Solar/PS process. While SO4·- played a dominant role at pH 6, it played a minor role at pH 9 due to the lower amount generated under alkaline conditions. The presence of chloride and ammonia negatively impacted the photodegradation of both compounds. In contrast, bicarbonate exhibited no effect on CAF but enhanced CFD degradation owing to its amino-acid-like structure, which has a higher reactivity toward CO3·-. Although total organic carbon (TOC) was partially mineralized after 6 h of operation, no Microtox® toxicity was observed.

The application of cardiac magnetic resonance imaging (CMR) in myocarditis after COVID-19 vaccines : Case series from single medical center in Taiwan.

During the coronavirus disease 2019 (COVID-19) pandemic, reports of vaccine-induced myocarditis, particularly messenger ribonucleic acid (mRNA)-based myocarditis, were widely spread. This case series describes various cases of COVID-19 vaccine-induced myocarditis confirmed by cardiac magnetic resonance imaging (MRI), including those who were administered rare protein-based vaccines. Eleven patients comprising eight males and three females with suspected myocarditis underwent cardiac MRI at Taichung Veterans General Hospital between October 2021 and May 2022. The median age of the patients was 33.5 years old (range: 22-57 years). The onset of myocarditis was mainly observed following mRNA vaccine inoculation. One patient received the MVC-COV1901 vaccine, a unique protein-based COVID-19 vaccine in Taiwan, and met the 2018 Lake Louise Criteria for the diagnosis of myocarditis, confirmed by cardiac MRI. Most patients reported chest discomfort after receiving various vaccine types. Among four patients with reduced left ventricular ejection fraction (LVEF), two showed LVEF restoration during the follow-up period, and the other two were lost to follow-up. Cardiac MRI characterizes myocardial features such as edema, inflammation, and fibrosis, and has been proven to diagnose myocarditis accurately with a sensitivity of 87.5% and a specificity of 96.2% according to the 2018 Lake Louise criteria. This diagnosis was achieved without invasive procedures such as endomyocardial biopsy or coronary angiography.

Reduction of Blood Loss by Means of the Cavitron Ultrasonic Surgical Aspirator for Thoracoscopic Salvage Anatomic Lung Resections.

In centrally located lung tumors, salvage pulmonary resections pose challenges due to adhesions between the pulmonary parenchyma, chest wall, and hilum. This study aimed to investigate the surgical outcomes associated with Cavitron Ultrasonic Surgical Aspirator (CUSA) usage in thoracoscopic salvage pulmonary resections. Patients with centrally located advanced-stage lung tumors who underwent salvage anatomic resections following systemic or radiotherapy were included. They were categorized into CUSA and non-CUSA groups, and perioperative parameters and surgical outcomes were analyzed. Results: The study included 7 patients in the CUSA group and 15 in the non-CUSA group. Despite a longer median surgical time in the CUSA group (3.8 h vs. 6.0 h, p = 0.021), there was a significant reduction in blood loss (100 mL vs. 250 mL, p = 0.014). Multivariate analyses revealed that the use of CUSA and radiotherapy had opposing effects on blood loss (ß: -296.7, 95% CI: -24.8 to -568.6, p = 0.034 and ß: 282.9, 95% CI: 19.7 to 546.3, p = 0.037, respectively). In conclusion, while using CUSA in the salvage anatomic resection of centrally located lung cancer may result in a longer surgical time, it is crucial in minimizing blood loss during the procedure.

Pulmonary Recruitment Prior to Intraoperative Multiple Pulmonary Ground-Glass Nodule Localization Increases the Localization Accuracy-A Retrospective Study.

The standard treatment for early-stage lung cancer is complete tumor excision by limited resection of the lung. Preoperative localization is used before video-assisted thoracoscopic surgery (VATS) to improve the accuracy of pulmonary nodule excision. However, lung atelectasis and hypoxia resulting from controlling apnea during the localization procedure may affect the localization accuracy. Pre-procedural pulmonary recruitment may improve the respiratory mechanics and oxygenation during localization. In this study, we investigated the potential benefits of pre-localization pulmonary recruitment prior to pulmonary ground-glass nodule localization in a hybrid operating room. We hypothesized that pre-localization pulmonary recruitment would increase the localization accuracy, improve oxygenation, and prevent the need for re-inflation during the localization procedure. We retrospectively enrolled patients with multiple pulmonary nodule localizations before surgical intervention in our hybrid operating room. We compared the localization accuracy between patients who had undergone pre-procedure pulmonary recruitment and patients who had not. Saturation, re-inflation rate, apnea time, procedure-related pneumothorax, and procedure time were also recorded as secondary outcomes. The patients who had undergone pre-procedure recruitment had better saturation, shorter procedure time, and higher localization accuracy. The pre-procedure pulmonary recruitment maneuver was effective in increasing regional lung ventilation, leading to improved oxygenation and localization accuracy.

Evolution of reactive species and their contribution to the removal of ketamine and amine-containing pharmaceuticals during the sunlight/chlorine process.

Sunlight-induced photoirradiation of chlorine (sunlight/chlorine) can be observed in outdoor swimming pools and open-channel disinfection units for wastewater treatment. In this study, the degradation of ketamine, an environmentally persistent pharmaceutical, under sunlight irradiation in the presence of a low concentration of chlorine (1 mg/L as Cl2) was investigated to elucidate the evolution of reactive species and their contribution to ketamine removal. •OH dominates the initial stage of sunlight/chlorine; however, after chlorine depletion, reactions still progress with an observed rate constant (kobs = 7.6 ± 0.50 × 10-3 min-1) an order of magnitude higher than photolysis alone (kobs = 2.9 ± 0.15 × 10-4 min-1). When chlorine is depleted, O3 becomes the major reactant that degrades ketamine. High O3 yields were found in both sunlight/HOCl (12.5 ± 0.5% at pH 5) and sunlight/ClO- (10 ± 1% at pH 10) systems. At sub-µM levels, O3 resulted in substantial removal of ketamine, and even faster rates were observed in the presence of sunlight. A kinetic model was also established, and evaluate time-dependent concentration levels during sunlight/chlorine. The model simulation showed that the cumulative O3 concentration could reach 0.91 µM, and O3 contributed 31% ketamine removal during the sunlight/chlorine process. Primary and secondary amine functional groups were demonstrated to be the reaction sites of O3; other pharmaceuticals, such as atenolol and metoprolol, underwent similar phenomena. In addition, the experimental and model results further indicated that sunlight/ClO2- or ClO2 also participates in the degradation of ketamine with a minor role; trace amounts (below nM level) of ClO2- and ClO2 were estimated by the simulation.

Structure-transferring edge-enhanced grid dehazing network.

The problem of image dehazing has received a great deal of attention in the computer vision community over the past two decades. Under haze conditions, due to the scattering of water vapor and dust particles in the air, the sharpness of the image is seriously reduced, making it difficult for many computer vision systems, such as those for object detection, object recognition, surveillance, driver assistance, etc. to do further process and operation. However, the previous dehazing methods usually have shortcomings such as poor brightness, color cast, removal of uncleanliness, halos, artifacts, and blurring. To address these problems, we propose a novel Structure-transferring Edge-enhanced Grid Dehazing Network (SEGDNet) in this study. An edge-preserving smoothing operator, a guided filter, is used to efficiently decompose the images into low-frequency image structure and high-frequency edges. The Low-frequency Grid Dehazing Subnetwork (LGDSn) is proposed to effectively preserve the low-frequency structure while dehazing. The High-frequency Edge Enhancement Subnetwork (HEESn) is also proposed to enhance the edges and details while removing the noise. The Low-and-High frequency Fusion Subnetwork (L&HFSn) is used to fuse the low-frequency and high-frequency results to obtain the final dehazed image. The experimental results on synthetic and real-world datasets demonstrate that our method outperforms the state-of-the-art methods in both qualitative and quantitative evaluations.

Incorporating Patient Preferences into a Decision-Making Model of Hand Trauma Reconstruction.

BACKGROUND: Few studies have addressed patient preferences in emergent surgical decision making. AIM OF THE STUDY: Analyzing patient preferences for hand trauma reconstruction to propose a decision-making model. METHODS: A conjoint analysis survey was developed with Sawtooth Software. Three common flaps-i.e., a cross-finger flap (CFF), a dorsal metacarpal artery perforator flap (DMAPF), and an arterialized venous flap (AVF)-were listed as treatment alternatives. Five attributes corresponding to these flaps were recovery time, total procedure, postoperative care methods, postoperative scar condition, and complication rate. Utility and importance scores were generated from the software, and preference characteristics were evaluated using cluster analysis. RESULTS: The survey was completed by 197 participants with hand trauma. Complication risk received the highest importance score (42.87%), followed by scar condition (21.55%). Cluster analysis classified the participants as "conservative," "practical," and "dual-concern". The dual-concern and conservative groups had more foreign laborers and highly educated participants, respectively, than the other groups. Most participants in the conservative and practical groups preferred DMAPF, whereas those in the dual-concern group favored CFF. Our proposed model consisted of shared decision-making and treatment recommendation pathways. CONCLUSION: Incorporating patient preferences into the decision-making model can strengthen patient-centered care. Further research on the applications of the proposed model is warranted.

Correction: Artificial Intelligence-Based Prediction of Lung Cancer Risk Using Nonimaging Electronic Medical Records: Deep Learning Approach.

[This corrects the article DOI: 10.2196/26256.].

Artificial Intelligence-Based Prediction of Lung Cancer Risk Using Nonimaging Electronic Medical Records: Deep Learning Approach.

BACKGROUND: Artificial intelligence approaches can integrate complex features and can be used to predict a patient's risk of developing lung cancer, thereby decreasing the need for unnecessary and expensive diagnostic interventions. OBJECTIVE: The aim of this study was to use electronic medical records to prescreen patients who are at risk of developing lung cancer. METHODS: We randomly selected 2 million participants from the Taiwan National Health Insurance Research Database who received care between 1999 and 2013. We built a predictive lung cancer screening model with neural networks that were trained and validated using pre-2012 data, and we tested the model prospectively on post-2012 data. An age- and gender-matched subgroup that was 10 times larger than the original lung cancer group was used to assess the predictive power of the electronic medical record. Discrimination (area under the receiver operating characteristic curve [AUC]) and calibration analyses were performed. RESULTS: The analysis included 11,617 patients with lung cancer and 1,423,154 control patients. The model achieved AUCs of 0.90 for the overall population and 0.87 in patients ≥55 years of age. The AUC in the matched subgroup was 0.82. The positive predictive value was highest (14.3%) among people aged ≥55 years with a pre-existing history of lung disease. CONCLUSIONS: Our model achieved excellent performance in predicting lung cancer within 1 year and has potential to be deployed for digital patient screening. Convolution neural networks facilitate the effective use of EMRs to identify individuals at high risk for developing lung cancer.

Enhanced sorption of the UV filter 4-methylbenzylidene camphor on aged PET microplastics from both experimental and theoretical perspectives.

In this study, the morphology and sorption behavior of polyethylene terephthalate (PET) microplastics during the aging process are investigated. To clarify the sorption mechanism of aged PET microplastics, the common sunblock 4-methylbenzylidene camphor (4-MBC) was chosen as the target contaminant, and UV irradiation was used for the laboratory aging simulation. The results show that oxygen-containing functional groups (carboxylic, carbonyl, ketone and hydroxyl groups) increase on the surface of aged PET microplastics. Based on density functional theory (DFT) simulations, the camphor part of 4-MBC acts as a hydrogen bond acceptor, whereas the carboxylic group on aged PET microplastics acts as a hydrogen bond donor. The formation of hydrogen bonding causes increased sorption of 4-MBC on aged PET microplastics. The sorption capacity increased from 5 to 11 µg g-1 for 50 ppb 4-MBC with 100 mg PET microplastics after a five-day aging process. Other environmental factors that affect sorption were also identified; a higher pH value and the presence of salinity reduced the amount of sorption. The sorption of virgin PET ranged from 8.0 to 3.4 µg g-1 and the sorption of aged PET ranged from 22 to 5 µg g-1 at pH 4 to 10. In the presence of salinity (10% seawater), the virgin PET sorption dropped to 2.1 µg g-1 while the aged PET sorption dropped to 4 µg g-1. A similar phenomenon was also observed in the sorption behavior under natural sunlight (the sorption of PET increased from 0.4 to 0.8 µg g-1 after 6 months of aging). The potential risk to ecosystems of aged PET microplastics under prolonged sunlight exposure in the natural environment could be greater than that predicted for virgin microplastics.

Deep Learning Approach for the Development of a Novel Predictive Model for Prostate Cancer.

We developed a deep learning approach for accurate prediction of PCA patients one year earlier with minimal features from electronic health records. The area under the receiver operating curve for prediction of PCA was 0.94. Moreover, the sensitivity and specificity of CNN were 0.87 and 0.88, respectively.

Engineering a Ga-Gradient by One-Step Sputtering to Achieve Over 15% Efficiency of Cu(In,Ga)Se2 Flexible Solar Cells without Post-selenization.

A one-step sputtering process using a quaternary target has been demonstrated to be a simple route to form Cu(In,Ga)Se2 (CIGSe) absorber without post-selenization; however, the lack of a Ga-grading structure in the CIGSe absorber confines its efficiency. Here, we demonstrate a one-step cosputtering process to control the Ga profile in the CIGSe absorber on flexible stainless steel substrates. Special attention was paid to the formation of second phases and their effects on the cell performance. Although the normal Ga-grading and efficiency enhancement could be achieved by cosputtering of CIGSe and Ga2Se3 targets, high-energy ion bombardment during the sputtering process might cause the decomposition of the Ga2Se3 target, leading to the formation of Ga2O3 in the CIGSe absorber, which gradually degraded the device performance. We replaced the Ga2Se3 target with a stoichiometric CuGaSe2 target for cosputtering, which can further enhance the cell efficiency due to the elimination of Ga2O3. However, when the Ga content at the back side of CIGSe is further increased by raising the deposition power of the CuGaSe2 target, the phase separation of CuGaSe2 may take place, resulting in the formation of Cu2-XSe and CuGaSe2 at the back side of the CIGSe absorber; therefore, the recombination at the back side is increased. By cosputtering a CIGSe target with a Cu-deficient CuGaSe2 target, we can suppress the formation of second phases and achieve designable normal grading, leading to the highest efficiency of 15.63% without post-selenization on flexible substrates.

Statins use and its impact in EGFR-TKIs resistance to prolong the survival of lung cancer patients: A Cancer registry cohort study in Taiwan.

Statins have been shown to be a beneficial treatment as chemotherapy and target therapy for lung cancer. This study aimed to investigate the effectiveness of statins in combination with epidermal growth factor receptor-tyrosine kinase inhibitor therapy for the resistance and mortality of lung cancer patients. A population-based cohort study was conducted using the Taiwan Cancer Registry database. From January 1, 2007, to December 31, 2012, in total 792 non-statins and 41 statins users who had undergone EGFR-TKIs treatment were included in this study. All patients were monitored until the event of death or when changed to another therapy. Kaplan-Meier estimators and Cox proportional hazards regression models were used to calculate overall survival. We found that the mortality was significantly lower in patients in the statins group compared with patients in the non-statins group (4-y cumulative mortality, 77.3%; 95% confidence interval (CI), 36.6%-81.4% vs. 85.5%; 95% CI, 78.5%-98%; P = .004). Statin use was associated with a reduced risk of death in patients the group who had tumor sizes <3 cm (hazard ratio [HR], 0.51, 95% CI, 0.29-0.89) and for patients in the group who had CCI scores <3 (HR, 0.6; 95% CI, 0.41-0.88; P = .009). In our study, statins were found to be associated with prolonged survival time in patients with lung cancer who were treated with EGFR-TKIs and played a synergistic anticancer role.

Assessment of Deep Learning Using Nonimaging Information and Sequential Medical Records to Develop a Prediction Model for Nonmelanoma Skin Cancer.

IMPORTANCE: A prediction model for new-onset nonmelanoma skin cancer could enhance prevention measures, but few patient data-driven tools exist for more accurate prediction. OBJECTIVE: To use machine learning to develop a prediction model for incident nonmelanoma skin cancer based on large-scale, multidimensional, nonimaging medical information. DESIGN, SETTING, AND PARTICIPANTS: This study used a database comprising 2 million randomly sampled patients from the Taiwan National Health Insurance Research Database from January 1, 1999, to December 31, 2013. A total of 1829 patients with nonmelanoma skin cancer as their first diagnosed cancer and 7665 random controls without cancer were included in the analysis. A convolutional neural network, a deep learning approach, was used to develop a risk prediction model. This risk prediction model used 3-year clinical diagnostic information, medical records, and temporal-sequential information to predict the skin cancer risk of a given patient within the next year. Stepwise feature selection was also performed to investigate important and determining factors of the model. Statistical analysis was performed from November 1, 2016, to October 31, 2018. MAIN OUTCOMES AND MEASURES: Sensitivity, specificity, and area under the receiver operating characteristic (AUROC) curve were used to evaluate the performance of the models. RESULTS: A total of 1829 patients (923 women [50.5%] and 906 men [49.5%]; mean [SD] age, 65.3 [15.7] years) with nonmelanoma skin cancer and 7665 random controls without cancer (3951 women [51.5%] and 3714 men [48.4%]; mean [SD] age, 47.5 [17.3] years) were included in the analysis. The 1-year incident nonmelanoma skin cancer risk prediction model using sequential diagnostic information and drug prescription information as a time-incorporated feature matrix could attain an AUROC of 0.89 (95% CI, 0.87-0.91), with a mean (SD) sensitivity of 83.1% (3.5%) and mean (SD) specificity of 82.3% (4.1%). Carcinoma in situ of skin (AUROC, 0.867; -2.80% loss) and other chronic comorbidities (eg, degenerative osteopathy [AUROC, 0.872; -2.32% loss], hypertension [AUROC, 0.879; -1.53% loss], and chronic kidney insufficiency [AUROC, 0.879; -1.52% loss]) served as more discriminative factors for the prediction. Medications such as trazodone, acarbose, systemic antifungal agents, statins, nonsteroidal anti-inflammatory drugs, and thiazide diuretics were the top-ranking discriminative features in the model; each led to more than a 1% decrease of the AUROC when eliminated individually (eg, trazodone AUROC, 0.868; -2.67% reduction; acarbose AUROC, 0.870; -2.50 reduction; and systemic antifungal agents AUROC, 0.875; -1.99 reduction). CONCLUSIONS AND RELEVANCE: The findings of this study suggest that a risk prediction model may have potential predictive factors for nonmelanoma skin cancer. This model may help health care professionals target high-risk populations for more intensive skin cancer preventive methods.

Development of Deep Learning Algorithm for Detection of Colorectal Cancer in EHR Data.

We aimed to develop a deep learning model for the prediction of the risk of advanced colorectal cancer in Taiwanese adults. We collected data of 58152 patients from the Taiwan National Health Insurance database from 1999 to 2013. All patients' comorbidities and medications history were included in the development of the convolution neural network (CNN) model. We also used 3-year medical data of all patients before the diagnosed colorectal cancer (CRC) as the dimensional time in the model. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were computed to measure the performance of the model. The results showed the mean (SD) of AUC of the model was 0.922 (0.004). Moreover, the performance of the model observed the sensitivity of 0.837, specificity of 0.867, and 0.532 for PPV value. Our study utilized CNN to develop a prediction model for CRC, based on non-image and multi-dimensional medical records.

Surface zwitterionization on versatile hydrophobic interfaces via a combined copolymerization/self-assembling process.

Amphiphilic zwitterionic copolymers have a severe problem of solubility, which restricts the feasibility and development of antifouling coatings. In this work, zwitterionic polymers were grafted via a novel modification method, namely, in situ self-assembling coating (ISC), which consists of synthesizing and simultaneously coating a copolymer onto a surface. This resolves the dissolution problem of amphiphilic zwitterionic copolymers. Here, the ISC method was applied to a copolymer composed of hydrophobic styrene (ST) and hydrophilic sulfobetaine methacrylate (SBMA). Under optimized conditions of concentration, molar ratio, and reaction time, the amphiphilic copolymer, poly(styrene-co-sulfobetaine methacrylate) (PS-PSBMA), can be coated onto the substrate surface. Ultralow protein adsorption from single-protein solutions and reduction of attachments from human blood platelets, erythrocytes, leukocytes, tissue cells, and bacteria were achieved. The mechanism of ISC was explained by carrying out time-dependent fibrinogen adsorption tests, along with particle size measurements in the polymerization bath. Furthermore, the ISC method was applied to versatile hydrophobic materials including polypropylene (PP), poly(dimethylsiloxane) (PDMS), and poly(tetrafluoroethylene) (PTFE). This work introduces a unique, convenient, and efficient method for synthesizing and coating amphiphilic zwitterionic polymers in a single step for antifouling applications in complex media.

Ecotoxicological effect of ketamine: Evidence of acute, chronic and photolysis toxicity to Daphnia magna.

Ketamine has been increasingly used in medicine and has the potential for abuse or illicit use around the world. Ketamine cannot be removed by conventional wastewater treatment plants. Although ketamine and its metabolite norketamine have been detected to a significant degree in effluents and aquatic environments, their ecotoxicity effects in aquatic organisms remain undefined. In this study, we investigated the acute toxicity of ketamine and its metabolite, along with the chronic reproductive toxicity of ketamine (5-100µg/L) to Daphnia magna. Multiple environmental scenarios were also evaluated, including drug mixtures and sunlight irradiation toxicity. Ketamine and norketamine caused acute toxicity to D. magna, with half lethal concentration (LC50) values of 30.93 and 25.35mg/L, respectively, after 48h of exposure. Irradiated solutions of ketamine (20mg/L) significantly increased the mortality of D. magna; pre-irradiation durations up to 2h rapidly increased the death rate to 100%. A new photolysis byproduct (M.W. 241) of norketamine that accumulates during irradiation was identified for the first time. The relevant environmental concentration of ketamine produced significant reproductive toxicity effects in D. magna, as revealed by the reduction of the number of total live offspring by 33.6-49.8% (p < 0.05). The toxicity results indicate that the environmental hazardous risks of the relevant ketamine concentration cannot be ignored and warrant further examination.