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Purpose: To compare the accuracy of 14 formulas in calculating intraocular lens (IOL) power in extremely long eyes with axial length (AL) over 30.0 mm.In this retrospective study, 211 eyes (211 patients) with ALs > 30.0 mm were successfully treated with cataract surgery without complications. Ocular biometric parameters were obtained from IOLMaster 700. Fourteen formulas were evaluated using the optimized A constants: Barrett Universal II (BUII), Kane, Emmetropia Verifying Optical (EVO) 2.0, PEARL-DGS, T2, SRK/T, Holladay 1, Holladay 2, Haigis and Wang-Koch AL adjusted formulas (SRK/Tmodified-W/K, Holladay 1modified-W/K, Holladay 1NP-modified-W/K, Holladay 2modified-W/K, Holladay 2NP-modified-W/K). The mean prediction error (PE) and standard deviation (SD), mean absolute errors (MAE), median absolute errors (MedAE), and the percentage of prediction errors (PEs) within ± 0.25 D, ± 0.50 D, ± 1.00 D were analyzed.The Kane formula had the smallest MAE (0.43 D) and MedAE (0.34 D). The highest percentage of PE within ± 0.25 D was for EVO 2.0 (37.91%) and the Holladay 1NP-modified-W/K formulas (37.91%). The Kane formula had the highest percentage of PEs in the range of ± 0.50, ± 0.75, ± 1.00, and ± 2.00 D. There was no significant difference in PEs within ± 0.25, ± 0.50 ± 0.75 and ± 1.00 D between BUII, Kane, EVO 2.0 and Wang-Koch AL adjusted formulas (P > .05) by using Cochran’s Q test. The Holladay 2modified-W/K formula has the lowest percentage of hyperopic outcomes (29.38%).The BUII, Kane, EVO 2.0 and Wang-Koch AL adjusted formulas have comparable accuracy for IOL power calculation in eyes with ALs > 30.0 mm.Methods: To compare the accuracy of 14 formulas in calculating intraocular lens (IOL) power in extremely long eyes with axial length (AL) over 30.0 mm.In this retrospective study, 211 eyes (211 patients) with ALs > 30.0 mm were successfully treated with cataract surgery without complications. Ocular biometric parameters were obtained from IOLMaster 700. Fourteen formulas were evaluated using the optimized A constants: Barrett Universal II (BUII), Kane, Emmetropia Verifying Optical (EVO) 2.0, PEARL-DGS, T2, SRK/T, Holladay 1, Holladay 2, Haigis and Wang-Koch AL adjusted formulas (SRK/Tmodified-W/K, Holladay 1modified-W/K, Holladay 1NP-modified-W/K, Holladay 2modified-W/K, Holladay 2NP-modified-W/K). The mean prediction error (PE) and standard deviation (SD), mean absolute errors (MAE), median absolute errors (MedAE), and the percentage of prediction errors (PEs) within ± 0.25 D, ± 0.50 D, ± 1.00 D were analyzed.The Kane formula had the smallest MAE (0.43 D) and MedAE (0.34 D). The highest percentage of PE within ± 0.25 D was for EVO 2.0 (37.91%) and the Holladay 1NP-modified-W/K formulas (37.91%). The Kane formula had the highest percentage of PEs in the range of ± 0.50, ± 0.75, ± 1.00, and ± 2.00 D. There was no significant difference in PEs within ± 0.25, ± 0.50 ± 0.75 and ± 1.00 D between BUII, Kane, EVO 2.0 and Wang-Koch AL adjusted formulas (P > .05) by using Cochran’s Q test. The Holladay 2modified-W/K formula has the lowest percentage of hyperopic outcomes (29.38%).The BUII, Kane, EVO 2.0 and Wang-Koch AL adjusted formulas have comparable accuracy for IOL power calculation in eyes with ALs > 30.0 mm.Results: To compare the accuracy of 14 formulas in calculating intraocular lens (IOL) power in extremely long eyes with axial length (AL) over 30.0 mm.In this retrospective study, 211 eyes (211 patients) with ALs > 30.0 mm were successfully treated with cataract surgery without complications. Ocular biometric parameters were obtained from IOLMaster 700. Fourteen formulas were evaluated using the optimized A constants: Barrett Universal II (BUII), Kane, Emmetropia Verifying Optical (EVO) 2.0, PEARL-DGS, T2, SRK/T, Holladay 1, Holladay 2, Haigis and Wang-Koch AL adjusted formulas (SRK/Tmodified-W/K, Holladay 1modified-W/K, Holladay 1NP-modified-W/K, Holladay 2modified-W/K, Holladay 2NP-modified-W/K). The mean prediction error (PE) and standard deviation (SD), mean absolute errors (MAE), median absolute errors (MedAE), and the percentage of prediction errors (PEs) within ± 0.25 D, ± 0.50 D, ± 1.00 D were analyzed.The Kane formula had the smallest MAE (0.43 D) and MedAE (0.34 D). The highest percentage of PE within ± 0.25 D was for EVO 2.0 (37.91%) and the Holladay 1NP-modified-W/K formulas (37.91%). The Kane formula had the highest percentage of PEs in the range of ± 0.50, ± 0.75, ± 1.00, and ± 2.00 D. There was no significant difference in PEs within ± 0.25, ± 0.50 ± 0.75 and ± 1.00 D between BUII, Kane, EVO 2.0 and Wang-Koch AL adjusted formulas (P > .05) by using Cochran’s Q test. The Holladay 2modified-W/K formula has the lowest percentage of hyperopic outcomes (29.38%).The BUII, Kane, EVO 2.0 and Wang-Koch AL adjusted formulas have comparable accuracy for IOL power calculation in eyes with ALs > 30.0 mm.Conclusions: To compare the accuracy of 14 formulas in calculating intraocular lens (IOL) power in extremely long eyes with axial length (AL) over 30.0 mm.In this retrospective study, 211 eyes (211 patients) with ALs > 30.0 mm were successfully treated with cataract surgery without complications. Ocular biometric parameters were obtained from IOLMaster 700. Fourteen formulas were evaluated using the optimized A constants: Barrett Universal II (BUII), Kane, Emmetropia Verifying Optical (EVO) 2.0, PEARL-DGS, T2, SRK/T, Holladay 1, Holladay 2, Haigis and Wang-Koch AL adjusted formulas (SRK/Tmodified-W/K, Holladay 1modified-W/K, Holladay 1NP-modified-W/K, Holladay 2modified-W/K, Holladay 2NP-modified-W/K). The mean prediction error (PE) and standard deviation (SD), mean absolute errors (MAE), median absolute errors (MedAE), and the percentage of prediction errors (PEs) within ± 0.25 D, ± 0.50 D, ± 1.00 D were analyzed.The Kane formula had the smallest MAE (0.43 D) and MedAE (0.34 D). The highest percentage of PE within ± 0.25 D was for EVO 2.0 (37.91%) and the Holladay 1NP-modified-W/K formulas (37.91%). The Kane formula had the highest percentage of PEs in the range of ± 0.50, ± 0.75, ± 1.00, and ± 2.00 D. There was no significant difference in PEs within ± 0.25, ± 0.50 ± 0.75 and ± 1.00 D between BUII, Kane, EVO 2.0 and Wang-Koch AL adjusted formulas (P > .05) by using Cochran’s Q test. The Holladay 2modified-W/K formula has the lowest percentage of hyperopic outcomes (29.38%).The BUII, Kane, EVO 2.0 and Wang-Koch AL adjusted formulas have comparable accuracy for IOL power calculation in eyes with ALs > 30.0 mm. [ABSTRACT FROM AUTHOR]

Multi-pass single-point incremental forming achieves large-angle part processing through multiple forming, and as the number of forming times increases, the number of forming defects on the workpiece also increases, significantly affecting the forming quality of the workpiece. This study focuses on multi-pass single-point incremental forming defects, investigating the effects of machining paths and different variable parameters on forming defects. It comprehensively analyses the effects of axial and radial compensation machining paths on forming defects through simulations and experiments. The results show that the greater the axial compensation, the more severe the wall thickness reduction and the fewer the sink mark defects. After radial compensation, the minimum wall thickness of the formed parts is reduced, and the larger the radial compensation, the more obvious the sink mark defects. The effects of variable pass angle, tool diameter, and layer feed rate on forming defects were also investigated. The results show that a decrease in the angle between passes reduces the minimum wall thickness of the workpiece. Furthermore, gradually increasing the tool diameter and decreasing the feed rate can increase the wall thickness of the workpiece; gradually decreasing the angle between passes increases the number of sink defects, whereas gradually increasing the tool diameter can reduce the number of sink defects. The sink defect is the smallest with the same layer feed rate. The effectiveness and accuracy of the simulation results have been verified through experiments, and this is of great significance for improving the forming accuracy of workpieces. [ABSTRACT FROM AUTHOR]

Vascular dementia (VaD) has a serious impact on the patients' quality of life. Icariin (Ica) possesses neuroprotective potential for treating VaD, yet its oral bioavailability and blood–brain barrier (BBB) permeability remain challenges. This research introduced a PEG–PLGA-loaded chitosan hydrogel-based binary formulation tailored for intranasal delivery, enhancing the intracerebral delivery efficacy of neuroprotective agents. The formulation underwent optimization to facilitate BBB crossing, with examinations conducted on its particle size, morphology, drug-loading capacity, in vitro release, and biodistribution. Using the bilateral common carotid artery occlusion (BCCAO) rat model, the therapeutic efficacy of this binary formulation was assessed against chitosan hydrogel and PEG–PLGA nanoparticles loaded with Ica. Post-intranasal administration, enhanced cognitive function was evident in chronic cerebral hypoperfusion (CCH) rats. Further mechanistic evaluations, utilizing immunohistochemistry (IHC), RT-PCR, and ELISA, revealed augmented transcription of synaptic plasticity-associated proteins like SYP and PSD-95, and a marked reduction in hippocampal inflammatory markers such as IL-1β and TNF-α, highlighting the formulation's promise in alleviating cognitive impairment. The brain-derived neurotrophic factor (BDNF)/tropomyosin related kinase B (TrkB) pathway was activated significantly in the binary formulation compared with the other two. Our study demonstrates that the intranasal application of chitosan hydrogel loaded with Ica-encapsulated PEG–PLGA could effectively deliver Ica into the brain and enhance its neuroprotective effect. [ABSTRACT FROM AUTHOR]

Purpose: As an important component of the microenvironment, the gastric microbiota and its metabolites are associated with tumour occurrence, progression, and metastasis. However, the relationship between the gastric microbiota and the development of gastric cancer is unclear. The present study investigated the role of the gastric mucosa microbiome and metabolites as aetiological factors in gastric carcinogenesis.Gastric biopsies from different stomach microhabitats (n = 70) were subjected to 16S rRNA gene sequencing, and blood samples (n = 95) were subjected to untargeted metabolome (gas chromatography‒mass spectrometry, GC‒MS) analyses. The datasets were analysed using various bioinformatics approaches.The microbiota diversity and community composition markedly changed during gastric carcinogenesis. High Helicobacter. pylori colonization modified the overall diversity and composition of the microbiota associated with gastritis and cancer in the stomach. Most importantly, analysis of the functional features of the microbiota revealed that nitrate reductase genes were significantly enriched in the tumoral microbiota, while urease-producing genes were significantly enriched in the microbiota of H. pylori-positive patients. A panel of 81 metabolites was constructed to discriminate gastric cancer patients from gastritis patients, and a panel of 15 metabolites was constructed to discriminate H. pylori-positivepatients from H. pylori-negative patients. receiver operator characteristic (ROC) curve analysis identified a series of gastric microbes and plasma metabolites as potential biomarkers of gastric cancer.The present study identified a series of signatures that may play important roles in gastric carcinogenesis and have the potential to be used as biomarkers for diagnosis and for the surveillance of gastric cancer patients with minimal invasiveness.Methods: As an important component of the microenvironment, the gastric microbiota and its metabolites are associated with tumour occurrence, progression, and metastasis. However, the relationship between the gastric microbiota and the development of gastric cancer is unclear. The present study investigated the role of the gastric mucosa microbiome and metabolites as aetiological factors in gastric carcinogenesis.Gastric biopsies from different stomach microhabitats (n = 70) were subjected to 16S rRNA gene sequencing, and blood samples (n = 95) were subjected to untargeted metabolome (gas chromatography‒mass spectrometry, GC‒MS) analyses. The datasets were analysed using various bioinformatics approaches.The microbiota diversity and community composition markedly changed during gastric carcinogenesis. High Helicobacter. pylori colonization modified the overall diversity and composition of the microbiota associated with gastritis and cancer in the stomach. Most importantly, analysis of the functional features of the microbiota revealed that nitrate reductase genes were significantly enriched in the tumoral microbiota, while urease-producing genes were significantly enriched in the microbiota of H. pylori-positive patients. A panel of 81 metabolites was constructed to discriminate gastric cancer patients from gastritis patients, and a panel of 15 metabolites was constructed to discriminate H. pylori-positivepatients from H. pylori-negative patients. receiver operator characteristic (ROC) curve analysis identified a series of gastric microbes and plasma metabolites as potential biomarkers of gastric cancer.The present study identified a series of signatures that may play important roles in gastric carcinogenesis and have the potential to be used as biomarkers for diagnosis and for the surveillance of gastric cancer patients with minimal invasiveness.Results: As an important component of the microenvironment, the gastric microbiota and its metabolites are associated with tumour occurrence, progression, and metastasis. However, the relationship between the gastric microbiota and the development of gastric cancer is unclear. The present study investigated the role of the gastric mucosa microbiome and metabolites as aetiological factors in gastric carcinogenesis.Gastric biopsies from different stomach microhabitats (n = 70) were subjected to 16S rRNA gene sequencing, and blood samples (n = 95) were subjected to untargeted metabolome (gas chromatography‒mass spectrometry, GC‒MS) analyses. The datasets were analysed using various bioinformatics approaches.The microbiota diversity and community composition markedly changed during gastric carcinogenesis. High Helicobacter. pylori colonization modified the overall diversity and composition of the microbiota associated with gastritis and cancer in the stomach. Most importantly, analysis of the functional features of the microbiota revealed that nitrate reductase genes were significantly enriched in the tumoral microbiota, while urease-producing genes were significantly enriched in the microbiota of H. pylori-positive patients. A panel of 81 metabolites was constructed to discriminate gastric cancer patients from gastritis patients, and a panel of 15 metabolites was constructed to discriminate H. pylori-positivepatients from H. pylori-negative patients. receiver operator characteristic (ROC) curve analysis identified a series of gastric microbes and plasma metabolites as potential biomarkers of gastric cancer.The present study identified a series of signatures that may play important roles in gastric carcinogenesis and have the potential to be used as biomarkers for diagnosis and for the surveillance of gastric cancer patients with minimal invasiveness.Conclusion: As an important component of the microenvironment, the gastric microbiota and its metabolites are associated with tumour occurrence, progression, and metastasis. However, the relationship between the gastric microbiota and the development of gastric cancer is unclear. The present study investigated the role of the gastric mucosa microbiome and metabolites as aetiological factors in gastric carcinogenesis.Gastric biopsies from different stomach microhabitats (n = 70) were subjected to 16S rRNA gene sequencing, and blood samples (n = 95) were subjected to untargeted metabolome (gas chromatography‒mass spectrometry, GC‒MS) analyses. The datasets were analysed using various bioinformatics approaches.The microbiota diversity and community composition markedly changed during gastric carcinogenesis. High Helicobacter. pylori colonization modified the overall diversity and composition of the microbiota associated with gastritis and cancer in the stomach. Most importantly, analysis of the functional features of the microbiota revealed that nitrate reductase genes were significantly enriched in the tumoral microbiota, while urease-producing genes were significantly enriched in the microbiota of H. pylori-positive patients. A panel of 81 metabolites was constructed to discriminate gastric cancer patients from gastritis patients, and a panel of 15 metabolites was constructed to discriminate H. pylori-positivepatients from H. pylori-negative patients. receiver operator characteristic (ROC) curve analysis identified a series of gastric microbes and plasma metabolites as potential biomarkers of gastric cancer.The present study identified a series of signatures that may play important roles in gastric carcinogenesis and have the potential to be used as biomarkers for diagnosis and for the surveillance of gastric cancer patients with minimal invasiveness. [ABSTRACT FROM AUTHOR]