Fixed-bed adsorption studies of endocrine-disrupting compounds from water by using novel calcium-based metal-organic frameworks.

The presence of emerging water pollutants such as endocrine-disrupting compounds (EDCs), including 17-ethynylestradiol (EE2), bisphenol A (BPA), and perfluorooctanoic acid (PFOA), in contaminated water sources poses significant environmental and health challenges. This study aims to address this issue by investigating the efficiency of novel calcium-based metal-organic frameworks, known as mixed-linker calcium-based metal-organic frameworks (Ca-MIX), in adsorbing these endocrine-disrupting compounds. This study analyzed the influence of influent concentration, bed height, and flow rate on pollutant removal, with bed height emerging as a crucial factor. From the breakthrough curves, it was determined that the column maximum adsorption capacities followed the order of 17-ethynylestradiol (101.52 µg/g; 40%) > bisphenol A (99.07 µg/g; 39%) > perfluorooctanoic acid (81.28 µg/g; 32%). Three models were used to predict the adsorption process, with the Yan model outperforming the other models. This suggests the potential of mixed-linker calcium-based metal-organic frameworks for removing endocrine-disrupting compounds from water, using the Yan model as an effective predictor. Overall, this study provides valuable insights for the development of effective water treatment methods using mixed-linker calcium-based metal-organic frameworks to remove endocrine-disrupting compounds from contaminated water sources.

Predicting medical device failure: a promise to reduce healthcare facilities cost through smart healthcare management.

Background: The advancement of biomedical research generates myriad healthcare-relevant data, including medical records and medical device maintenance information. The COVID-19 pandemic significantly affects the global mortality rate, creating an enormous demand for medical devices. As information technology has advanced, the concept of intelligent healthcare has steadily gained prominence. Smart healthcare utilises a new generation of information technologies, such as the Internet of Things (loT), big data, cloud computing, and artificial intelligence, to completely transform the traditional medical system. With the intention of presenting the concept of smart healthcare, a predictive model is proposed to predict medical device failure for intelligent management of healthcare services. Methods: Present healthcare device management can be improved by proposing a predictive machine learning model that prognosticates the tendency of medical device failures toward smart healthcare. The predictive model is developed based on 8,294 critical medical devices from 44 different types of equipment extracted from 15 healthcare facilities in Malaysia. The model classifies the device into three classes; (i) class 1, where the device is unlikely to fail within the first 3 years of purchase, (ii) class 2, where the device is likely to fail within 3 years from purchase date, and (iii) class 3 where the device is likely to fail more than 3 years after purchase. The goal is to establish a precise maintenance schedule and reduce maintenance and resource costs based on the time to the first failure event. A machine learning and deep learning technique were compared, and the best robust model for smart healthcare was proposed. Results: This study compares five algorithms in machine learning and three optimizers in deep learning techniques. The best optimized predictive model is based on ensemble classifier and SGDM optimizer, respectively. An ensemble classifier model produces 77.90%, 87.60%, and 75.39% for accuracy, specificity, and precision compared to 70.30%, 83.71%, and 67.15% for deep learning models. The ensemble classifier model improves to 79.50%, 88.36%, and 77.43% for accuracy, specificity, and precision after significant features are identified. The result concludes although machine learning has better accuracy than deep learning, more training time is required, which is 11.49 min instead of 1 min 5 s when deep learning is applied. The model accuracy shall be improved by introducing unstructured data from maintenance notes and is considered the author's future work because dealing with text data is time-consuming. The proposed model has proven to improve the devices' maintenance strategy with a Malaysian Ringgit (MYR) cost reduction of approximately MYR 326,330.88 per year. Therefore, the maintenance cost would drastically decrease if this smart predictive model is included in the healthcare management system.

Development of a single-run liquid chromatography-mass spectrometry analysis for the detection of 11 multiclass contaminants of emerging concern using a direct filtration method.

In toxicological analysis, the analytical validation method is important to assess the exact risk of contaminants of emerging concern in the environment. Syringe filters are mainly used to remove impurities from sample solutions. However, the loss of analyte to the syringe filter could be considerable, causing an underestimate of the analyte concentrations. The current study develops and validates simultaneous liquid chromatography-mass spectrometry analysis using a direct filtration method to detect four groups of contaminants of emerging concern. The adsorption of the analyte onto three different matrices and six types of syringe filters is reported. The lowest adsorption of analytes was observed in methanol (16.72%), followed by deionized water (48.19%) and filtered surface lake water (48.94%). Irrespective of the type of the matrices, the lowest average adsorption by the syringe filter was observed in the 0.45 µm polypropylene membrane (15.15%), followed by the 0.20 µm polypropylene membrane (16.10%), the 0.20 µm regenerated cellulose (16.15%), the 0.20 µm polytetrafluoroethylene membrane (47.38%), the 0.45 µm nylon membrane (64.87%) and the 0.20 µm nylon membrane (71.30%). In conclusion, the recommended syringe filter membranes for contaminants of emerging concern analysis are polypropylene membranes and regenerated cellulose, regardless of the matrix used.

Machine Learning Application of Transcranial Motor-Evoked Potential to Predict Positive Functional Outcomes of Patients.

Intraoperative neuromonitoring (IONM) has been used to help monitor the integrity of the nervous system during spine surgery. Transcranial motor-evoked potential (TcMEP) has been used lately for lower lumbar surgery to prevent nerve root injuries and also to predict positive functional outcomes of patients. There were a number of studies that proved that the TcMEP signal's improvement is significant towards positive functional outcomes of patients. In this paper, we explored the possibilities of using a machine learning approach to TcMEP signal to predict positive functional outcomes of patients. With 55 patients who underwent various types of lumbar surgeries, the data were divided into 70 : 30 and 80 : 20 ratios for training and testing of the machine learning models. The highest sensitivity and specificity were achieved by Fine KNN of 80 : 20 ratio with 87.5% and 33.33%, respectively. In the meantime, we also tested the existing improvement criteria presented in the literature, and 50% of TcMEP improvement criteria achieved 83.33% sensitivity and 75% specificity. But the rigidness of this threshold method proved unreliable in this study when different datasets were used as the sensitivity and specificity dropped. The proposed method by using machine learning has more room to advance with a larger dataset and various signals' features to choose from.

An overview of the effects of nanoplastics on marine organisms.

The ubiquity and detrimental effects of plastics in the environment have become global environmental concerns over the past decade. Intensive anthropogenic activities, such as urbanisation, industrialisation and increasing population density, have resulted in increased plastic pollution in the environment. Recently, nanoplastics have received increased research attention and concern because of their potential adverse effects on marine organisms. However, the potential ecological issues associated with nanoplastics are not yet fully understood because of the insufficient and limited research conducted to date on baseline data, exposure and associated risks for marine organisms. This review highlights an understanding of the nature and characteristics of nanoplastics, as well as the occurrence of nanoplastics in the marine environment. In the future, the effects of nanoplastics on marine organisms may directly or indirectly influence human health. Thus, this review also highlights the effects of nanoplastics on marine organisms. An overview and insights into the occurrence of nanoplastics in marine environments and their potential effects on marine organisms will facilitate the preventative interventions and measures of nanoplastics pollution in the marine environment.

Occurrence, potential sources and ecological risk estimation of microplastic towards coastal and estuarine zones in Malaysia.

Extensive global plastic production has led to microplastic (MP) pollution of marine ecosystems. This study analysed the abundance of MPs in the surface water of tropical coastal and estuarine zones in Malaysia affected by rapid urbanisation and intense human activity. It also estimated the risk posed by MPs to the marine environment. Mean MP abundance ranged from 2.10 to 6.80 particles/L. Fourier-transform infrared spectroscopic analysis found that the MP polymers were dominated by cellophane (54%), followed by polyester (33%) and polyethylene (2%). The risk posed by MPs was estimated with the risk quotient (RQ) method which found no potential ecological risk to both coastal and estuarine areas (RQ < 1). This study will serve as a baseline for future monitoring of MP pollution of marine water to assess the impact of heavily urbanised coastal and estuarine zones.

Single-Stage Debridement and Spinal Instrumentation for Salmonella Spondylodiscitis of Thoracic Vertebra.

For patients with pyogenic spondylodiscitis, medical therapy with antibiotics is the first line of treatment. Response to antibiotics can be assessed by improvement of symptoms, reduction in inflammatory markers, and radiological evidence of infection resolution with magnetic resonance imaging (MRI). We report a case of a 60-year-old man who presented with intermittent fever and persistent back pain. He was initially treated with an intravenous antibiotic for sepsis secondary to Salmonella enteritidis bacteremia. His spine MRI showed compression of the T10 vertebra with vertebral abnormality suggestive of metastases. He showed no clinical improvement after a course of intravenous antibiotics. Following that, debridement and spinal instrumentation of the thoracic vertebra were done in single-stage surgery.

Transcranial Electrical Motor Evoked Potential in Predicting Positive Functional Outcome of Patients after Decompressive Spine Surgery: Review on Challenges and Recommendations towards Objective Interpretation.

Spine surgeries impose risk to the spine's surrounding anatomical and physiological structures especially the spinal cord and the nerve roots. Intraoperative neuromonitoring (IONM) is a technology developed to monitor the integrity of the spinal cord and the nerve roots via the surgery. Transcranial motor evoked potential (TcMEP) (one of the IONM modalities) is adopted to monitor the integrity of the motor pathway of the spinal cord and the motor nerve roots. Recent research suggested that the IONM is conducive as a prognostic tool towards the patient's functional outcome. This paper summarizes the researches of IONM being adopted as a prognostic tool. In addition, this paper highlights the problems associated with the signal parameters as the improvement criteria in the previous researches. Lastly, we review the challenges of TcMEP to achieve a prognostic tool focusing on the factors that could interfere with the generation of a stable TcMEP response. The final section will discuss recommendations for IONM technology to achieve an objective prognostic tool.

Occurrence, abundance, and distribution of microplastics pollution: an evidence in surface tropical water of Klang River estuary, Malaysia.

Microplastics have been considered as contaminants of emerging concern due to ubiquity in the environment; however, the occurrence of microplastics in river estuaries is scarcely investigated. The Klang River estuary is an important ecosystem that receives various contaminants from urbanised, highly populated areas and the busiest maritime centre in Selangor, Malaysia. This study investigates the abundance and characteristics of microplastics in surface water of the Klang River estuary. The abundance of microplastics ranged from 0.5 to 4.5 particles L-1 with a mean abundance of 2.47 particles L-1. There is no correlation between the abundance of microplastics and physicochemical properties, while there is a strong correlation between salinity and conductivity. The microplastics were characterised with a stereomicroscope and attenuated total reflection-Fourier transform infrared spectroscopy to analyse size, shape, colour, and polymer composition. The microplastics in the surface water were predominantly in the 300-1000 µm size class, followed by > 1000 µm and < 300 µm, and were mostly transparent fibres, fragments, and pellets. Polyamide and polyethylene were the main polymer types in the composition of the microplastics, suggesting that the microplastics originated from heavily urbanised and industrial locations such as the port, jetty, and residential areas. The widespread occurrence of microplastics in the environment and subsequent penetration of aquatic food webs may pose a serious threat to organisms. This study provides baseline data and a framework for further investigation of microplastic contamination in estuaries.

Microplastic pollution in tropical estuary gastropods: Abundance, distribution and potential sources of Klang River estuary, Malaysia.

Microplastics receive global attention due to its strong presence in marine and freshwater organisms. Yet, there are few studies on estuarine organisms. This baseline study evaluates the abundance and distribution of microplastics in the gastropods of a tropical estuary in Selangor, Malaysia. The abundance of microplastics ranged from 0.50 to 1.75 particles/g or from 0.25 to 0.88 particles/individual. The variation in microplastic distribution between the upper and lower estuary indicates that the microplastics originated from the urbanised area of the Klang River estuary. Microplastic sizes varied from 30 to 1850 µm, with the majority being between 300 and 1000 µm (57%). Characteristics of microplastics were dominant for fibres (91%) with black colour (50%). Polyethylene-propylene-diene (PE-PDM) and polyester were the main polymer materials. Assessing the contamination of gastropods by microplastics provides insight into the possibility of utilising gastropods as bioindicators that could be used for monitoring and baseline studies.

Comparison of honey and natural ointment based on honey-tea tree oil on the healing of diabetic foot ulcer

OBJECTIVES: This study aimed to compare the impact of honey and natural ointment based on honey-Tea Tree Oil. METHODS: This was a quasi-experimental study with a pre-post control group design approach. The sample consisted of 27 respondents in two different wound care clinics. The instruments used were the Leg Ulcer Measurement Tool (LUMT) measured on day 1, 7 and 14. Data was analyzed using paired t-test, pooled t-test, and multiple regression test. RESULTS: Most participants were female, the average age was 50.89 years, the average duration of illness for type II diabetes was 3.56 and had current infection. The results showed a significant difference in LUMT score between before and after treatment of wounds healing in the two intervention groups (p-value=0.000). CONCLUSION: Honey and natural ointment based on honey - tea tree oil significantly contributed to the healing of Diabetic Foot Ulcer

No disponible

A Dual-Functional Catalyst for Cascade Meerwein-Pondorf-Verley Reduction and Dehydration of 4'-Methoxypropiophenone to Anethole.

Anethole is an ingredient in many flavours, fragrances and pharmaceutical formulations. To reduce the dependence of its supply on natural oils, a green route for anethole synthesis was designed on the basis of Meerwein-Pondorf-Verley (MPV) reduction and dehydration of 4'-methoxypropiophenone. The one-pot cascade reactions were heterogeneously catalysed by dual-functional Zr-MSU-3, a predominantly Lewis-acidic catalyst with a Si/Zr ratio of 10 and pores with sizes in the range of 3.2-4.2 nm. The use of 2-pentanol as solvent and hydrogen donor for the MPV reduction was advantageous, as its high boiling point enhances the rate of the reactions, especially the dehydration of the MPV product, 1-(4-methoxyphenyl)-propan-1-ol. This dispenses with the need for a strong acid catalyst that could result in by-products of acid-catalysed reactions. Anethole yields of 91 % with a trans/cis isomer ratio of about 92:8, similar to that of natural anethole, were obtained. In comparison, microporous Zr-beta (Si/Zr 12.5) gave lower activity owing to pore-size constraints. Hence, through design of the reactions and catalyst, 4'-methoxypropiophenone can be efficiently converted to anethole in a sustainable and green manner.

Synthesis, spectral studies and biological evaluation of 2-aminonicotinic acid metal complexes.

We synthesized 2-aminonicotinic acid (2-ANA) complexes with metals such as Co(II), Fe(III), Ni(II), Mn(II), Zn(II), Ag(I),Cr(III), Cd(II) and Cu(II) in aqueous media. The complexes were characterized and elucidated using FT-IR, UV-Vis, a fluorescence spectrophotometer and thermo gravimetric analysis (TGA). TGA data showed that the stoichiometry of complexes was 1:2 metal/ligand except for Ag(I) and Mn(II) where the ratio was 1:1. The metal complexes showed varied antibacterial, fungicidal and nematicidal activities. The silver and zinc complexes showed highest activity against Bacillus subtilis and Bacillus licheniformis respectively. Fusarium oxysporum was highly susceptible to nickel and copper complexes whereas Macrophomina phaseolina was completely inert to the complexes. The silver and cadmium complexes were effective against the root-knot nematode Meloidogyne javanica.

H. pylori acutely inhibits gastric secretion by activating CGRP sensory neurons coupled to stimulation of somatostatin and inhibition of histamine secretion.

Acute Helicobacter pylori infection produces hypochlorhydria. The decrease in acid facilitates survival of the bacterium and its colonization of the stomach. The present study was designed to identify the pathways in oxyntic mucosa by which acute H. pylori infection inhibits acid secretion. In rat fundic sheets in an Ussing chamber, perfusion of the luminal surface with H. pylori in spent broth (10(3)-10(8) cfu/ml) or spent broth alone (1:10(5) to 1:10(0) final dilution) caused a concentration-dependent increase in somatostatin (SST; maximal: 200 ± 20 and 194 ± 9% above basal; P < 0.001) and decrease in histamine secretion (maximal: 45 ± 5 and 48 ± 2% below basal; P < 0.001); the latter was abolished by SST antibody, implying that changes in histamine secretion reflected changes in SST secretion. Both responses were abolished by the axonal blocker tetrodotoxin (TTX), the sensory neurotoxin capsaicin, or the CGRP antagonist CGRP8-37, implying that the reciprocal changes in SST and histamine secretion were due to release of CGRP from sensory neurons. In isolated rabbit oxyntic glands, H. pylori inhibited basal and histamine-stimulated acid secretion in a concentration-dependent manner; the responses were not affected by TTX or SST antibody, implying that H. pylori can directly inhibit parietal cell function. In conclusion, acute administration of H. pylori is capable of inhibiting acid secretion directly as well as indirectly by activating intramural CGRP sensory neurons coupled to stimulation of SST and inhibition of histamine secretion. Activation of neural pathways provides one explanation as to how initial patchy colonization of the superficial gastric mucosa by H. pylori can acutely inhibit acid secretion.

Amylin, released from the gastric fundus, stimulates somatostatin and thus inhibits histamine and acid secretion in mice.

BACKGROUND & AIMS: Amylin, a peptide that displays 50% homology with calcitonin gene-related peptide (CGRP), is colocalized with somatostatin in endocrine cells of the gastric fundus. The present study was designed to determine the mechanism of action of amylin on gastric exocrine and endocrine secretion. METHODS: Acid secretion was measured in the isolated mouse stomach by titration. Somatostatin and histamine secretion were measured in rat fundic segments by radioimmunoassay. RESULTS: In isolated mouse stomach, amylin caused a concentration-dependent decrease in acid secretion. In rat fundic segments, amylin and CGRP each caused a concentration-dependent increase in somatostatin and a decrease in histamine secretion. Changes in histamine secretion induced by amylin reflected changes in somatostatin secretion and could be abolished by addition of somatostatin antibody. Both the somatostatin and the histamine responses to amylin were abolished by the selective amylin antagonist AC187 but were unaffected by the CGRP antagonist CGRP8-37. In contrast, the responses to CGRP were abolished by CGRP8-37 but were unaffected by AC187. AC187 alone decreased somatostatin and increased histamine in fundic segments and increased acid secretion in isolated stomach, indicating that endogenous amylin participates in the regulation of gastric endocrine (somatostatin and histamine) and exocrine (acid) secretion. CONCLUSIONS: In gastric fundus, release of amylin from somatostatin cells interacts with distinct amylin receptors to enhance somatostatin secretion via an autocrine pathway that leads to inhibition of histamine and acid secretion.