Kuala Lumpur of Malaysia,as a tropical city,has experienced a notable decline in its critical urban green infrastructure(UGI)due to rapid urbanization and haphazard development.The decrease of UGI,especially natural f...Kuala Lumpur of Malaysia,as a tropical city,has experienced a notable decline in its critical urban green infrastructure(UGI)due to rapid urbanization and haphazard development.The decrease of UGI,especially natural forest and artificial forest,may reduce the diversity of ecosystem services and the ability of Kuala Lumpur to build resilience in the future.This study analyzed land use and land cover(LULC)and UGI changes in Kuala Lumpur based on Landsat satellite images in 1990,2005,and 2021and employed the overall accuracy and Kappa coefficient to assess classification accuracy.LULC was categorized into six main types:natural forest,artificial forest,grassland,water body,bare ground,and built-up area.Satellite images in 1990,2005,and 2021 showed the remarkable overall accuracy values of 91.06%,96.67%,and 98.28%,respectively,along with the significant Kappa coefficient values of 0.8997,0.9626,and 0.9512,respectively.Then,this study utilized Cellular Automata and Markov Chain model to analyze the transition of different LULC types during 1990-2005 and 1990-2021 and predict LULC types in 2050.The results showed that natural forest decreased from 15.22%to 8.20%and artificial forest reduced from 18.51%to 15.16%during 1990-2021.Reductions in natural forest and artificial forest led to alterations in urban surface water dynamics,increasing the risk of urban floods.However,grassland showed a significant increase from 7.80%to 24.30%during 1990-2021.Meanwhile,bare ground increased from 27.16%to 31.56%and built-up area increased from 30.45%to 39.90%during 1990-2005.In 2021,built-up area decreased to 35.10%and bare ground decreased to 13.08%,indicating a consistent dominance of built-up area in the central parts of Kuala Lumpur.This study highlights the importance of integrating past,current,and future LULC changes to improve urban ecosystem services in the city.展开更多
Objective:To evaluate the effectiveness of direct-acting antivirals(DAAs)in patients with chronic hepatitis C,assess changes in liver function and hepatic fibrosis following treatment,and identify independent predicto...Objective:To evaluate the effectiveness of direct-acting antivirals(DAAs)in patients with chronic hepatitis C,assess changes in liver function and hepatic fibrosis following treatment,and identify independent predictors of treatment failure.Methods:This retrospective cohort study included patients who received DAA therapy at Hospital Kuala Lumpur between January 2020 and December 2023.Sustained virologic response(SVR)was assessed at least 12 weeks post-treatment by reverse transcription-polymerase chain reaction for hepatitis C virus(HCV)RNA.Demographic,clinical,and laboratory data were collected and analyzed.Multiple logistic regression analysis was performed to identify independent predictors of treatment failure.Results:A total of 335 patients in the study.The overall SVR rate was 89%.After achieving SVR,significant improvements were observed in liver enzyme levels and non-invasive liver fibrosis scores,whereas the overall Model for End-Stage Liver Disease(MELD)scores remained unchanged.Significant independent predictors of treatment failure included non-compliance with DAA therapy[adjusted odds ratio(aOR)68.3;95%confidence interval(95%CI)16.3-285.0;P<0.001],treatment with sofosbuvir/velpatasvir(aOR 6.1;95%CI 1.4-26.5;P=0.015),MELD score of 10-15(aOR 4.6;95%CI 1.1-18.2;P=0.031),HCV genotype 3 infection(aOR 4.5;95%CI 1.1-17.6;P=0.031),and elevated serum total bilirubin level(aOR 1.1;95%CI 1.0-1.1;P=0.003).Conclusions:DAA therapy yielded a high SVR rate,and treatment failure was strongly associated with non-adherence to therapy and advanced liver disease.These findings underscore the necessity of adherence support,early diagnosis,and individualized clinical management to optimize treatment outcomes in patients with chronic hepatitis C.展开更多
Honeycombing Lung(HCL)is a chronic lung condition marked by advanced fibrosis,resulting in enlarged air spaces with thick fibrotic walls,which are visible on Computed Tomography(CT)scans.Differentiating between normal...Honeycombing Lung(HCL)is a chronic lung condition marked by advanced fibrosis,resulting in enlarged air spaces with thick fibrotic walls,which are visible on Computed Tomography(CT)scans.Differentiating between normal lung tissue,honeycombing lungs,and Ground Glass Opacity(GGO)in CT images is often challenging for radiologists and may lead to misinterpretations.Although earlier studies have proposed models to detect and classify HCL,many faced limitations such as high computational demands,lower accuracy,and difficulty distinguishing between HCL and GGO.CT images are highly effective for lung classification due to their high resolution,3D visualization,and sensitivity to tissue density variations.This study introduces Honeycombing Lungs Network(HCL Net),a novel classification algorithm inspired by ResNet50V2 and enhanced to overcome the shortcomings of previous approaches.HCL Net incorporates additional residual blocks,refined preprocessing techniques,and selective parameter tuning to improve classification performance.The dataset,sourced from the University Malaya Medical Centre(UMMC)and verified by expert radiologists,consists of CT images of normal,honeycombing,and GGO lungs.Experimental evaluations across five assessments demonstrated that HCL Net achieved an outstanding classification accuracy of approximately 99.97%.It also recorded strong performance in other metrics,achieving 93%precision,100%sensitivity,89%specificity,and an AUC-ROC score of 97%.Comparative analysis with baseline feature engineering methods confirmed the superior efficacy of HCL Net.The model significantly reduces misclassification,particularly between honeycombing and GGO lungs,enhancing diagnostic precision and reliability in lung image analysis.展开更多
Nanotechnology has revolutionized drug delivery,particularly through nanoformulations of phytoconstituents,enhancing their therapeutic potential.Despite their broad bioactivities,plant-based compounds often suffer fro...Nanotechnology has revolutionized drug delivery,particularly through nanoformulations of phytoconstituents,enhancing their therapeutic potential.Despite their broad bioactivities,plant-based compounds often suffer from poor bioavailability and stability.Nanoformulations address these limitations by improving solubility,targeted delivery,and controlled release.This approach opens new possibilities for treating chronic diseases like cancer,diabetes,and neurodegenerative disorders.This review aims to examine recent advancements in nanotechnology-based formulation strategies designed to enhance the delivery,stability,and therapeutic efficacy of phytochemicals and also discusses regulatory issues,safety concerns,scalability,and cost-effectiveness.Emphasis was placed on nanoformulation techniques employed for key phytoconstituents such as curcumin,resveratrol,epigallocatechin gallate,and quercetin.The most commonly employed nanocarriers included polymeric nanoparticles,solid lipid nanoparticles,and liposomes.These formulations significantly improved the solubility,stability,and controlled release profiles of phytochemicals.In vitro and in vivo studies demonstrated enhanced anti-inflammatory,anticancer,and antioxidant activities.Moreover,surface-modified and targeted nanoparticles showed promise in increasing site-specific targeting and enhancing bioavailability of the encapsulated compounds.Nanoformulations present a promising strategy for overcoming the pharmacokinetic limitations of phytochemicals.Despite encouraging preclinical results,further studies are needed to address issues related to long-term safety,clinical efficacy,and regulatory approval for successful clinical translation.展开更多
The functional relationships between flow （veh/km）, density （veh/h） and speed （kin/h） in traffic congestion have a long history of research. However, their findings and techniques persist to be relevant to this ...The functional relationships between flow （veh/km）, density （veh/h） and speed （kin/h） in traffic congestion have a long history of research. However, their findings and techniques persist to be relevant to this day. The analysis is pertinent, particularly in finding the best fit for the three major highways in Malaysia, namely the KL-Karak Highway, KL-Seremban Highway and KL-Ipoh Highway. The trans-logarithm function of density-speed model was compared to the classical models of Greenshields, Greenberg, Underwood and Drake et al. using data provided by the Transport Statistics Malaysia 2014. The results of regression analysis revealed that the Greenshields and Greenberg models were statistically significant. The trans-logarithm function was also tested and the results were nonetheless without exception. Its usefulness in addition to statistical significance related to the derived economic concepts of maximum speed and the related number of vehicles, flow and density and the limits of free speed were relevant in comparing the individual levels of traffic congestion between highways. For instance, KL-Karak Highway was least congested compared to KL-Seremban Highway and KL-Ipoh Highway. Their maximum speeds, based on three lanes carriage capacity of one direction, were 33.4 km/h for KL-Karak, 15.9 km/h for KL-Seremban, and 21.1 km/h for KL-Ipoh. Their corresponding flows were approximated at 1080.9 veh/h, 1555.4 veh/h, and 1436.6 veh/h.展开更多
基金supported by the Malaysia-Japan International Institute of Technology(MJIIT),Universiti Teknologi Malaysia.
文摘Kuala Lumpur of Malaysia,as a tropical city,has experienced a notable decline in its critical urban green infrastructure(UGI)due to rapid urbanization and haphazard development.The decrease of UGI,especially natural forest and artificial forest,may reduce the diversity of ecosystem services and the ability of Kuala Lumpur to build resilience in the future.This study analyzed land use and land cover(LULC)and UGI changes in Kuala Lumpur based on Landsat satellite images in 1990,2005,and 2021and employed the overall accuracy and Kappa coefficient to assess classification accuracy.LULC was categorized into six main types:natural forest,artificial forest,grassland,water body,bare ground,and built-up area.Satellite images in 1990,2005,and 2021 showed the remarkable overall accuracy values of 91.06%,96.67%,and 98.28%,respectively,along with the significant Kappa coefficient values of 0.8997,0.9626,and 0.9512,respectively.Then,this study utilized Cellular Automata and Markov Chain model to analyze the transition of different LULC types during 1990-2005 and 1990-2021 and predict LULC types in 2050.The results showed that natural forest decreased from 15.22%to 8.20%and artificial forest reduced from 18.51%to 15.16%during 1990-2021.Reductions in natural forest and artificial forest led to alterations in urban surface water dynamics,increasing the risk of urban floods.However,grassland showed a significant increase from 7.80%to 24.30%during 1990-2021.Meanwhile,bare ground increased from 27.16%to 31.56%and built-up area increased from 30.45%to 39.90%during 1990-2005.In 2021,built-up area decreased to 35.10%and bare ground decreased to 13.08%,indicating a consistent dominance of built-up area in the central parts of Kuala Lumpur.This study highlights the importance of integrating past,current,and future LULC changes to improve urban ecosystem services in the city.
文摘Objective:To evaluate the effectiveness of direct-acting antivirals(DAAs)in patients with chronic hepatitis C,assess changes in liver function and hepatic fibrosis following treatment,and identify independent predictors of treatment failure.Methods:This retrospective cohort study included patients who received DAA therapy at Hospital Kuala Lumpur between January 2020 and December 2023.Sustained virologic response(SVR)was assessed at least 12 weeks post-treatment by reverse transcription-polymerase chain reaction for hepatitis C virus(HCV)RNA.Demographic,clinical,and laboratory data were collected and analyzed.Multiple logistic regression analysis was performed to identify independent predictors of treatment failure.Results:A total of 335 patients in the study.The overall SVR rate was 89%.After achieving SVR,significant improvements were observed in liver enzyme levels and non-invasive liver fibrosis scores,whereas the overall Model for End-Stage Liver Disease(MELD)scores remained unchanged.Significant independent predictors of treatment failure included non-compliance with DAA therapy[adjusted odds ratio(aOR)68.3;95%confidence interval(95%CI)16.3-285.0;P<0.001],treatment with sofosbuvir/velpatasvir(aOR 6.1;95%CI 1.4-26.5;P=0.015),MELD score of 10-15(aOR 4.6;95%CI 1.1-18.2;P=0.031),HCV genotype 3 infection(aOR 4.5;95%CI 1.1-17.6;P=0.031),and elevated serum total bilirubin level(aOR 1.1;95%CI 1.0-1.1;P=0.003).Conclusions:DAA therapy yielded a high SVR rate,and treatment failure was strongly associated with non-adherence to therapy and advanced liver disease.These findings underscore the necessity of adherence support,early diagnosis,and individualized clinical management to optimize treatment outcomes in patients with chronic hepatitis C.
文摘Honeycombing Lung(HCL)is a chronic lung condition marked by advanced fibrosis,resulting in enlarged air spaces with thick fibrotic walls,which are visible on Computed Tomography(CT)scans.Differentiating between normal lung tissue,honeycombing lungs,and Ground Glass Opacity(GGO)in CT images is often challenging for radiologists and may lead to misinterpretations.Although earlier studies have proposed models to detect and classify HCL,many faced limitations such as high computational demands,lower accuracy,and difficulty distinguishing between HCL and GGO.CT images are highly effective for lung classification due to their high resolution,3D visualization,and sensitivity to tissue density variations.This study introduces Honeycombing Lungs Network(HCL Net),a novel classification algorithm inspired by ResNet50V2 and enhanced to overcome the shortcomings of previous approaches.HCL Net incorporates additional residual blocks,refined preprocessing techniques,and selective parameter tuning to improve classification performance.The dataset,sourced from the University Malaya Medical Centre(UMMC)and verified by expert radiologists,consists of CT images of normal,honeycombing,and GGO lungs.Experimental evaluations across five assessments demonstrated that HCL Net achieved an outstanding classification accuracy of approximately 99.97%.It also recorded strong performance in other metrics,achieving 93%precision,100%sensitivity,89%specificity,and an AUC-ROC score of 97%.Comparative analysis with baseline feature engineering methods confirmed the superior efficacy of HCL Net.The model significantly reduces misclassification,particularly between honeycombing and GGO lungs,enhancing diagnostic precision and reliability in lung image analysis.
文摘Nanotechnology has revolutionized drug delivery,particularly through nanoformulations of phytoconstituents,enhancing their therapeutic potential.Despite their broad bioactivities,plant-based compounds often suffer from poor bioavailability and stability.Nanoformulations address these limitations by improving solubility,targeted delivery,and controlled release.This approach opens new possibilities for treating chronic diseases like cancer,diabetes,and neurodegenerative disorders.This review aims to examine recent advancements in nanotechnology-based formulation strategies designed to enhance the delivery,stability,and therapeutic efficacy of phytochemicals and also discusses regulatory issues,safety concerns,scalability,and cost-effectiveness.Emphasis was placed on nanoformulation techniques employed for key phytoconstituents such as curcumin,resveratrol,epigallocatechin gallate,and quercetin.The most commonly employed nanocarriers included polymeric nanoparticles,solid lipid nanoparticles,and liposomes.These formulations significantly improved the solubility,stability,and controlled release profiles of phytochemicals.In vitro and in vivo studies demonstrated enhanced anti-inflammatory,anticancer,and antioxidant activities.Moreover,surface-modified and targeted nanoparticles showed promise in increasing site-specific targeting and enhancing bioavailability of the encapsulated compounds.Nanoformulations present a promising strategy for overcoming the pharmacokinetic limitations of phytochemicals.Despite encouraging preclinical results,further studies are needed to address issues related to long-term safety,clinical efficacy,and regulatory approval for successful clinical translation.
文摘The functional relationships between flow （veh/km）, density （veh/h） and speed （kin/h） in traffic congestion have a long history of research. However, their findings and techniques persist to be relevant to this day. The analysis is pertinent, particularly in finding the best fit for the three major highways in Malaysia, namely the KL-Karak Highway, KL-Seremban Highway and KL-Ipoh Highway. The trans-logarithm function of density-speed model was compared to the classical models of Greenshields, Greenberg, Underwood and Drake et al. using data provided by the Transport Statistics Malaysia 2014. The results of regression analysis revealed that the Greenshields and Greenberg models were statistically significant. The trans-logarithm function was also tested and the results were nonetheless without exception. Its usefulness in addition to statistical significance related to the derived economic concepts of maximum speed and the related number of vehicles, flow and density and the limits of free speed were relevant in comparing the individual levels of traffic congestion between highways. For instance, KL-Karak Highway was least congested compared to KL-Seremban Highway and KL-Ipoh Highway. Their maximum speeds, based on three lanes carriage capacity of one direction, were 33.4 km/h for KL-Karak, 15.9 km/h for KL-Seremban, and 21.1 km/h for KL-Ipoh. Their corresponding flows were approximated at 1080.9 veh/h, 1555.4 veh/h, and 1436.6 veh/h.
