1.Hydrogen and Methane Breath Test: The Asian Neurogastroenterology andMotility Association Monograph
Yinglian XIAO ; Kewin T H SIAH ; Mengyu ZHANG ; Benjamin Wei Rong TAY ; Kee Huat CHUAH ; Victoria TAN ; Yen Po WANG ; Yingxuan CHEN ; Ling LIU ; Uday C GHOSHAL ; Justin C Y WU ; Xiaohua HOU
Journal of Neurogastroenterology and Motility 2026;32(2):150-171
Despite of the widespread use of hydrogen and methane breath test, the variability in testing protocols, gas measurement techniques, and interpretation criteria continues to challenge the reproducibility and comparability across centers, especially in the Asia-Pacific region. The Asian Neurogastroenterology and Motility Association hence presents the first Asian monograph guiding application and interpretation of breath test. The monograph was formulated according to the framework of indications, preparatory process, performance, and interpretation of results, as well as future direction for research.
2.Acute gastroenteritis in adults.
Wei Ling TAY ; Jaime Mei-Fong CHIEN ; Vijo POULOSE ; Choon How HOW ; Mark Chung Wai NG
Singapore medical journal 2025;66(8):457-461
3.Predictive Modeling of Symptomatic Intracranial Hemorrhage Following Endovascular Thrombectomy: Insights From the Nationwide TREAT-AIS Registry
Jia-Hung CHEN ; I-Chang SU ; Yueh-Hsun LU ; Yi-Chen HSIEH ; Chih-Hao CHEN ; Chun-Jen LIN ; Yu-Wei CHEN ; Kuan-Hung LIN ; Pi-Shan SUNG ; Chih-Wei TANG ; Hai-Jui CHU ; Chuan-Hsiu FU ; Chao-Liang CHOU ; Cheng-Yu WEI ; Shang-Yih YAN ; Po-Lin CHEN ; Hsu-Ling YEH ; Sheng-Feng SUNG ; Hon-Man LIU ; Ching-Huang LIN ; Meng LEE ; Sung-Chun TANG ; I-Hui LEE ; Lung CHAN ; Li-Ming LIEN ; Hung-Yi CHIOU ; Jiunn-Tay LEE ; Jiann-Shing JENG ;
Journal of Stroke 2025;27(1):85-94
Background:
and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking.
Methods:
This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance.
Results:
Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal.
Conclusions
The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.
4.Predictive Modeling of Symptomatic Intracranial Hemorrhage Following Endovascular Thrombectomy: Insights From the Nationwide TREAT-AIS Registry
Jia-Hung CHEN ; I-Chang SU ; Yueh-Hsun LU ; Yi-Chen HSIEH ; Chih-Hao CHEN ; Chun-Jen LIN ; Yu-Wei CHEN ; Kuan-Hung LIN ; Pi-Shan SUNG ; Chih-Wei TANG ; Hai-Jui CHU ; Chuan-Hsiu FU ; Chao-Liang CHOU ; Cheng-Yu WEI ; Shang-Yih YAN ; Po-Lin CHEN ; Hsu-Ling YEH ; Sheng-Feng SUNG ; Hon-Man LIU ; Ching-Huang LIN ; Meng LEE ; Sung-Chun TANG ; I-Hui LEE ; Lung CHAN ; Li-Ming LIEN ; Hung-Yi CHIOU ; Jiunn-Tay LEE ; Jiann-Shing JENG ;
Journal of Stroke 2025;27(1):85-94
Background:
and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking.
Methods:
This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance.
Results:
Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal.
Conclusions
The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.
5.Predictive Modeling of Symptomatic Intracranial Hemorrhage Following Endovascular Thrombectomy: Insights From the Nationwide TREAT-AIS Registry
Jia-Hung CHEN ; I-Chang SU ; Yueh-Hsun LU ; Yi-Chen HSIEH ; Chih-Hao CHEN ; Chun-Jen LIN ; Yu-Wei CHEN ; Kuan-Hung LIN ; Pi-Shan SUNG ; Chih-Wei TANG ; Hai-Jui CHU ; Chuan-Hsiu FU ; Chao-Liang CHOU ; Cheng-Yu WEI ; Shang-Yih YAN ; Po-Lin CHEN ; Hsu-Ling YEH ; Sheng-Feng SUNG ; Hon-Man LIU ; Ching-Huang LIN ; Meng LEE ; Sung-Chun TANG ; I-Hui LEE ; Lung CHAN ; Li-Ming LIEN ; Hung-Yi CHIOU ; Jiunn-Tay LEE ; Jiann-Shing JENG ;
Journal of Stroke 2025;27(1):85-94
Background:
and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking.
Methods:
This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance.
Results:
Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal.
Conclusions
The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.
7.Loss-of-Function Variant in the SMPD1 Gene in Progressive Supranuclear Palsy-Richardson Syndrome Patients of Chinese Ancestry
Shen-Yang LIM ; Ai Huey TAN ; Jia Nee FOO ; Yi Jayne TAN ; Elaine GY CHEW ; Azlina Ahmad ANNUAR ; Alfand Marl Dy CLOSAS ; Azalea PAJO ; Jia Lun LIM ; Yi Wen TAY ; Anis NADHIRAH ; Jia Wei HOR ; Tzi Shin TOH ; Lei Cheng LIT ; Jannah ZULKEFLI ; Su Juen NGIM ; Weng Khong LIM ; Huw R. MORRIS ; Eng-King TAN ; Adeline SL NG
Journal of Movement Disorders 2024;17(2):213-217
Lysosomal dysfunction plays an important role in neurodegenerative diseases, including Parkinson’s disease (PD) and possibly Parkinson-plus syndromes such as progressive supranuclear palsy (PSP). This role is exemplified by the involvement of variants in the GBA1 gene, which results in a deficiency of the lysosomal enzyme glucocerebrosidase and is the most frequently identified genetic factor underlying PD worldwide. Pathogenic variants in the SMPD1 gene are a recessive cause of Niemann–Pick disease types A and B. Here, we provide the first report on an association between a loss-of-function variant in the SMPD1 gene present in a heterozygous state (p.Pro332Arg/p.P332R, which is known to result in reduced lysosomal acid sphingomyelinase activity), with PSP-Richardson syndrome in three unrelated patients of Chinese ancestry.
9.Oral Presentation – Clinical and Translational Research
Choon Hoong Chung ; Yee Lynn Soh ; Thinaesh Manoharan ; Arwind Raj ; Dulmini Perera ; Htoo Htoo Kyaw Soe ; Nan Nitra Than ; Lilija Bancevica ; Žanna Kovalova ; Dzintars Ozols ; Ksenija Soldatenkova ; Lim Pyae Ying ; Tay Siow Phing ; Wong Jin Shyan ; Andrew Steven Sinsoon ; Nursabrina Alya Ricky Ramsis ; Nina Azwina Kimri ; Henry Rantai Gudum ; Man Le Ng ; Sze Er Lim ; Hui Yu Kim ; Yee Wan Lee ; Soo Kun Lim ; Sharven Raj ; Mohd Nasir Mohd Desa ; Nurul Syazrah Anuar ; Nurshahira Sulaiman ; Hui Chin Ting ; Zhi Ling Loo ; Choey Yee Lew ; Alfand Marl F Dy Closas ; Tzi Shin Toh ; Jia Wei Hor ; Yi Wen Tay ; Jia Lun Lim ; Lu Yian Tan ; Jie Ping Schee ; Lei Cheng Lit ; Ai Huey Tan ; Shen Yang Lim ; Zhu Shi Wong ; Nur Raziana binti Rozi ; Soo Kun Lim
International e-Journal of Science, Medicine and Education 2022;16(Suppl1):7-14
10.EPOSTER • DRUG DISCOVERY AND DEVELOPMENT
Marwan Ibrahim ; Olivier D LaFlamme ; Turgay Akay ; Julia Barczuk ; Wioletta Rozpedek-Kaminska ; Grzegorz Galita ; Natalia Siwecka ; Ireneusz Majsterek ; Sharmni Vishnu K. ; Thin Thin Wi ; Saint Nway Aye ; Arun Kumar ; Grace Devadason ; Fatin Aqilah Binti Ishak ; Goh Jia Shen ; Dhaniya A/P Subramaniam ; Hiew Ke Wei ; Hong Yan Ren ; Sivalingam Nalliah ; Nikitha Lalindri Mareena Senaratne ; Chong Chun Wie ; Divya Gopinath ; Pang Yi Xuan ; Mohamed Ismath Fathima Fahumida ; Muhammad Imran Bin Al Nazir Hussain ; Nethmi Thathsarani Jayathilake ; Sujata Khobragade ; Htoo Htoo Kyaw Soe ; Soe Moe ; Mila Nu Nu Htay ; Rosamund Koo ; Tan Wai Yee ; Wong Zi Qin ; Lau Kai Yee ; Ali Haider Mohammed ; Ali Blebil ; Juman Dujaili ; Alicia Yu Tian Tan ; Cheryl Yan Yen Ng ; Ching Xin Ni ; Michelle Ng Yeen Tan ; Kokila A/P Thiagarajah ; Justin Jing Cherg Chong ; Yong Khai Pang ; Pei Wern Hue ; Raksaini Sivasubramaniam ; Fathimath Hadhima ; Jun Jean Ong ; Matthew Joseph Manavalan ; Reyna Rehan ; Tularama Naidu ; Hansi Amarasinghe ; Minosh Kumar ; Sdney Jia Eer Tew ; Yee Sin Chong ; Yi Ting Sim ; Qi Xuan Ng ; Wei Jin Wong ; Shaun Wen Huey Lee ; Ronald Fook Seng Lee ; Wei Ni Tay ; Yi Tan ; Wai Yew Yang ; Shu Hwa Ong ; Yee Siew Lim ; Siddique Abu Nowajish ; Zobaidul Amin ; Umajeyam Anbarasan ; Lim Kean Ghee ; John Pinto ; Quek Jia Hui ; Ching Xiu Wei ; Dominic Lim Tao Ran ; Philip George ; Chandramani Thuraisingham ; Tan Kok Joon ; Wong Zhi Hang ; Freya Tang Sin Wei ; Ho Ket Li ; Shu Shuen Yee ; Goon Month Lim ; Wen Tien Tan ; Sin Wei Tang
International e-Journal of Science, Medicine and Education 2022;16(Suppl1):21-37


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