Objective:To establish the reference intervals (RIs) of systemic immune inflammatory index (SII), platelet to lymphocyte ratio (PLR), neutrophil to lymphocyte ratio (NLR), lymphocyte to monocyte ratio (LMR) and monocyte to lymphocyte ratio (MLR) in healthy pregnant women in Henan province, China.Methods:A retrospective analysis was conducted on the data of the healthy pregnant women without a history of adverse pregnancy events who participated in health check-ups from August 2016 to February 2019. A total of 4 016 healthy pregnant women were selected for establishing RIs. Data from healthy adult control group were derived from the healthy adult cohort in Henan established earlier by our team, and the Propensity Score Matching analysis was used and 3 595 healthy adult women and 3 595 healthy pregnant women to compare the indicators between the two groups. The RIs of the above indicators were established using the indirect method with a 95% confidence interval. The Tukey Rule was used to identify and remove outliers. The RIs were stratified and grouped based on the differences in each indicator during the pregnancy: SII: 3 929 cases, including 712 in the first trimester, 1 947 in the second trimester, and 1, 270 in the third trimester; PLR: 3 927 cases, no grouping; NLR: 3 925 cases, including 712 in the first trimester and 3 213 in the second and third trimesters; LMR: 3 925 cases, including 723 in the first trimester, 1 942 in the second trimester, and 1 260 in the third trimester; MLR: 3 904 cases, including 721 in the first trimester, 1 928 in the second trimester, and 1 255 in the third trimester. After the RIs were established, another 396 healthy pregnant women without a history of adverse pregnancy events who participated in health check-ups from February to April 2019 were selected for the validation of the RIs.Results:SII, NLR, LMR, MLR, and PLR differ significantly between healthy adult women and healthy pregnant women. There were significant differences in SII, LMR, and MLR among the three trimesters ( P<0.05). NLR in the first trimester was significantly lower than that in the second and third trimesters ( P<0.05), while there was no significant difference between the second and third trimester ( P=0.124). PLR only showed significant differences between the second and third trimester ( P<0.05), while no significant differences were found among the other groups. Based on the above results, the stratified RIs of each index in healthy pregnant population were established and verified. SII: first trimester (341-1 426)×10 9/L, second trimester (437-1 680)×10 9/L, third trimester (379-1 580)×10 9/L; PLR: 73-215; NLR: first trimester 1.78-5.60, second and third trimester 2.21-6.74; LMR: first trimester 2.20-6.61, second trimester 1.85-5.42, third trimester 1.63-4.82; MLR: first trimester 0.14-0.42, second trimester 0.17-0.49, third trimester 0.18-0.55. The rejection rate of 396 cases was less than 10%. Conclusions:The RIs of SII, NLR, LMR, MLR and PLR for healthy pregnant women in Hernan province of China were established and validated, and4 could be used in clinical practice.

Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.

Objective:To establish the reference intervals (RIs) of systemic immune inflammatory index (SII), platelet to lymphocyte ratio (PLR), neutrophil to lymphocyte ratio (NLR), lymphocyte to monocyte ratio (LMR) and monocyte to lymphocyte ratio (MLR) in healthy pregnant women in Henan province, China.Methods:A retrospective analysis was conducted on the data of the healthy pregnant women without a history of adverse pregnancy events who participated in health check-ups from August 2016 to February 2019. A total of 4 016 healthy pregnant women were selected for establishing RIs. Data from healthy adult control group were derived from the healthy adult cohort in Henan established earlier by our team, and the Propensity Score Matching analysis was used and 3 595 healthy adult women and 3 595 healthy pregnant women to compare the indicators between the two groups. The RIs of the above indicators were established using the indirect method with a 95% confidence interval. The Tukey Rule was used to identify and remove outliers. The RIs were stratified and grouped based on the differences in each indicator during the pregnancy: SII: 3 929 cases, including 712 in the first trimester, 1 947 in the second trimester, and 1, 270 in the third trimester; PLR: 3 927 cases, no grouping; NLR: 3 925 cases, including 712 in the first trimester and 3 213 in the second and third trimesters; LMR: 3 925 cases, including 723 in the first trimester, 1 942 in the second trimester, and 1 260 in the third trimester; MLR: 3 904 cases, including 721 in the first trimester, 1 928 in the second trimester, and 1 255 in the third trimester. After the RIs were established, another 396 healthy pregnant women without a history of adverse pregnancy events who participated in health check-ups from February to April 2019 were selected for the validation of the RIs.Results:SII, NLR, LMR, MLR, and PLR differ significantly between healthy adult women and healthy pregnant women. There were significant differences in SII, LMR, and MLR among the three trimesters ( P<0.05). NLR in the first trimester was significantly lower than that in the second and third trimesters ( P<0.05), while there was no significant difference between the second and third trimester ( P=0.124). PLR only showed significant differences between the second and third trimester ( P<0.05), while no significant differences were found among the other groups. Based on the above results, the stratified RIs of each index in healthy pregnant population were established and verified. SII: first trimester (341-1 426)×10 9/L, second trimester (437-1 680)×10 9/L, third trimester (379-1 580)×10 9/L; PLR: 73-215; NLR: first trimester 1.78-5.60, second and third trimester 2.21-6.74; LMR: first trimester 2.20-6.61, second trimester 1.85-5.42, third trimester 1.63-4.82; MLR: first trimester 0.14-0.42, second trimester 0.17-0.49, third trimester 0.18-0.55. The rejection rate of 396 cases was less than 10%. Conclusions:The RIs of SII, NLR, LMR, MLR and PLR for healthy pregnant women in Hernan province of China were established and validated, and4 could be used in clinical practice.

Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.

Pulmonary vein isolation (PVI) is a new ,effective and radical method to cure atrial fibrillation .Within a period after radiofrequency ablation (RFA) ,coagulation system is activated in patients ,then thrombus incident such as cerebral embolism may happen .The present article made a review on its mechanism .

OBJECTIVETo elucidate the inhibitory effects of recombinant Chinese scorpion neurotoxin BmK IM on seizures induced by pentylenetetrazol (PTZ) and the possible mechanism.

METHODSAfter purifying recombinant BmK IM from an E. coli cell line, its toxicity (both LD50 and minimum lethal dose) on rats was determined. BmK IM was then microinjected into the CA3 region of the right hippocampus and its ability to inhibit the effects of an intraperitoneal injection of PTZ was assessed. The effects of BmK IM on the electrophysiological properties of isolated CA3 pyramidal neurons were then studied using whole-cell patch clamp techniques.

RESULTSBmK IM can significantly prolong the latent period of epileptic seizures, decrease the degree of seizures, and decrease the frequency of epileptiform discharges induced by PTZ. At the same time, 24h after injection of BmK IM into the hippocampal tissue, BmK IM significantly reduces the concentration of the neurotransmitter glutamate and alleviates PTZ-induced lesions in the hippocampus. Whole-cell patch clamp recordings indicate that BmK IM inhibits INa of rat hippocampal neurons in a dose-dependent manner. BmK IM significantly shifts the activation curve of INa in a positive direction, indicating that BmK IM enhances the threshold potential of INa.

CONCLUSIONSBmK IM has significant anti-epileptic properties, and may prove useful as a drug in the therapy of epilepsy. The inhibitory effects of BmK IM on seizures caused by pentylenetetrazol might depend on reductions in the release of presynaptic glutamate via the blocking of Na+ channels.

Animals ; Glutamine ; secretion ; Hippocampus ; drug effects ; Male ; Microinjections ; Pentylenetetrazole ; Peptides ; administration & dosage ; therapeutic use ; Rats ; Rats, Sprague-Dawley ; Recombinant Proteins ; administration & dosage ; therapeutic use ; Scorpion Venoms ; administration & dosage ; therapeutic use ; Seizures ; chemically induced ; prevention & control ; Sodium Channels ; drug effects