Lupus nephritis in Chinese children--a territory-wide cohort study in Hong Kong

We report a multicenter study of Chinese children in Hong Kong with systemic lupus erythematosus (SLE) nephritis. Children were included if: they fulfilled the ACR criteria, had significant proteinuria or casturia, were Chinese and younger than 19 years and had been diagnosed with SLE between January 1990 and December 2003. Investigators in each center retrieved data on clinical features, biopsy reports, treatment and outcome of these patients. There were 128 patients (eight boys, 120 girls; mean age: 11.9+/-2.8 years). About 50% presented with multisystem illness and 40% with nephritic/nephrotic symptoms. Negative anti-dsDNA antibodies were found in 6% of the patients. Renal biopsy revealed WHO Class II, III, IV and V nephritis in 13 (10%), 22 (17%), 69 (54%) and 13 (10%) patients, respectively. The clinical severity of the nephritis did not accurately predict renal biopsy findings. The follow-up period ranged from 1 to 16.5 years (mean+/-SD: 5.76+/-3.61 years). During the study five patients died (two from lupus flare, one from cardiomyopathy, two from infections). Four patients had endstage renal failure (ESRF) (one died during a lupus flare). All deaths and end-stage renal failure occurred in the Class IV nephritis group. Chronic organ damage was infrequent in the survivors. The actuarial patient survival rates at 5, 10 and 15 years of age were 95.3, 91.8, and 91.8%, respectively. For Class IV nephritis patients, the survival rates without ESRF at 5, 10, and 15 years were 91.5, 82.3 and 76%, respectively. The survival and chronic morbidity rates of the Chinese SLE children in the present study are comparable to those of other published studies.postprin

Modelling Large Protein Complexes

AlphaFold [Jumper et al., 2021, Evans et al., 2022] is a deep learning-based method that can accurately predict the structure of single- and multiple-chain proteins. However, its accuracy decreases with an increasing number of chains, and GPU memory limits the size of protein complexes that can be predicted. Recently, Elofsson’s groupintroduced a Monte Carlo tree search method, MoLPC, that can predict the structure of large complexes from predictions of sub-components [Bryant et al., 2022b]. However, MoLPC cannot adjust for errors in the sub-component predictions and requires knowledge of the correct protein stoichiometry. Large protein complexes are responsible for many essential cellular processes, such as mRNA splicing [Will and Lührmann, 2011], protein degradation [Tanaka, 2009], and protein folding [Ditzel et al., 1998]. However, the lack of structural knowledge of many large protein complexes remains challenging. Only a fraction of the eukaryoticcore complexes in CORUM [Giurgiu et al., 2019] have homologous structures covering all chains in PDB, indicating a significant gap in our structural understanding of protein complexes. AlphaFold-Multimer [Evans et al., 2022] is the only deep learning method that can predict the structure of more than two protein chains, trained on proteins of up to 20 chains, and can predict complexes of up to a few thousand residues, where memory limitations come into play. Another approach, MoLPC, is to predict the structure of sub-components of large complexes and assemble them. It has shown that it is possible to manually assemble large complexes from dimers manually [Burke et al., 2021] or use Monte Carlo tree search [Bryant et al., 2022b]. One limitation of the previous MoLPC approach is its inability to account for errors in sub-component prediction. The addition of small errors in each sub-component can propagate to a significant error when building the entire complex, leading toMoLPC’s failure. To overcome this challenge, the Monte Carlo Tree Search algorithms in MoLPC2 is enhanced to assemble protein complexes while simultaneously predicting their stoichiometry. Using MoLPC2, we accurately predicted the structures of 50 out of 175 non-redundant protein complexes (TM-score >0.8), while MoLPC only predicted 30. It should be noted that improvements introduced in AlphaFold version 2.3 enable the prediction of larger complexes, and if stoichiometry is known, it can accurately predict the structures of 74 complexes. Our findings suggest that assembling symmetrical complexes from sub-components results in higher accuracy while assembling asymmetrical complexes remains challenging

Modelling Large Protein Complexes

AlphaFold [Jumper et al., 2021, Evans et al., 2022] is a deep learning-based method that can accurately predict the structure of single- and multiple-chain proteins. However, its accuracy decreases with an increasing number of chains, and GPU memory limits the size of protein complexes that can be predicted. Recently, Elofsson’s groupintroduced a Monte Carlo tree search method, MoLPC, that can predict the structure of large complexes from predictions of sub-components [Bryant et al., 2022b]. However, MoLPC cannot adjust for errors in the sub-component predictions and requires knowledge of the correct protein stoichiometry. Large protein complexes are responsible for many essential cellular processes, such as mRNA splicing [Will and Lührmann, 2011], protein degradation [Tanaka, 2009], and protein folding [Ditzel et al., 1998]. However, the lack of structural knowledge of many large protein complexes remains challenging. Only a fraction of the eukaryoticcore complexes in CORUM [Giurgiu et al., 2019] have homologous structures covering all chains in PDB, indicating a significant gap in our structural understanding of protein complexes. AlphaFold-Multimer [Evans et al., 2022] is the only deep learning method that can predict the structure of more than two protein chains, trained on proteins of up to 20 chains, and can predict complexes of up to a few thousand residues, where memory limitations come into play. Another approach, MoLPC, is to predict the structure of sub-components of large complexes and assemble them. It has shown that it is possible to manually assemble large complexes from dimers manually [Burke et al., 2021] or use Monte Carlo tree search [Bryant et al., 2022b]. One limitation of the previous MoLPC approach is its inability to account for errors in sub-component prediction. The addition of small errors in each sub-component can propagate to a significant error when building the entire complex, leading toMoLPC’s failure. To overcome this challenge, the Monte Carlo Tree Search algorithms in MoLPC2 is enhanced to assemble protein complexes while simultaneously predicting their stoichiometry. Using MoLPC2, we accurately predicted the structures of 50 out of 175 non-redundant protein complexes (TM-score >0.8), while MoLPC only predicted 30. It should be noted that improvements introduced in AlphaFold version 2.3 enable the prediction of larger complexes, and if stoichiometry is known, it can accurately predict the structures of 74 complexes. Our findings suggest that assembling symmetrical complexes from sub-components results in higher accuracy while assembling asymmetrical complexes remains challenging

Efficacy of low-magnitude high-frequency vibration (LMHFV) on musculoskeletal health of participants on wheelchair: a study protocol for a single-blinded randomised controlled study

Background Osteoporosis is an age-related disease with progressive loss of bone, leading to fragile bone. It is one of the major health issues in older adults and causes medical, social and economic impacts globally. Patients with osteoporosis have high risk of osteoporotic fractures. Low-magnitude high-frequency vibration (LMHFV) is a non-invasive biophysical intervention providing whole-body mechanical stimulation. Previous studies showed that LMHFV is beneficial to muscle strength, postural control, balancing ability, new bone formation, spinal bone mineral density (BMD) and blood circulation. During the LMHFV treatment, older adults need to stand upright on the platform for 20 min/day. However, some physically weak elderlies with poor musculoskeletal ability cannot stand for a long period. Therefore, the design of vibration platform is modified for the disabled patients to treat at sitting position and the efficacy of LMHFV on this group of elderlies will be verified. It is hypothesised that new design of LMHFV is beneficial to wheelchair users in terms of vertebral BMD, muscle health and musculoskeletal functions.Methods This study is a single-blinded randomised controlled trial to investigate the effect of LMHFV on vertebral BMD, muscle health, balancing ability and functional ability in wheelchair users (mainly on wheelchair for outdoor activities). Healthy elderlies aged 65 years or above with walking difficulties and using wheelchair are eligible. Exclusion criteria are those: (1) who cannot stand and walk independently, (2) who have vibration treatment before, (3) with malignancy, (4) with acute fractures or severe osteoarthritis, (5) with cardiovascular concern such as with pacemaker in situ, (6) with chronic inflammatory conditions known to affect muscle metabolism such as rheumatoid arthritis and (7) with high frequency of physical activities, such as participants who participated in regular exercise five times a week or more. Recruited participants will be randomised to either LMHFV or control group. Participant assigned to LMHFV group will receive LMHFV (35 Hz, 0.3g (g=gravitational acceleration), 20 min/day, at least three times/week) for 6 months. The primary outcome is BMD at the lumbar spine to be assessed by dual-energy X-ray absorptiometry that is clinically recommended for the diagnosis of osteoporosis. All primary and secondary outcome assessments for all groups will be performed in the investigators’ institute at baseline and 6 months post treatment.Discussion This study aims to investigate the effects of LMHFV on wheelchair users. The findings of this study will help to confirm the efficacy of LMHFV on vertebral BMD, muscle health, balancing ability and functional outcomes in wheelchair using elderlies. LMHFV therapy is an intervention strategy that is easy to implement at the community healthcare level or individually at home that has previously been proven to reduce fall risk and muscle strength at the lower limb. The ultimate goal is to improve their bone and muscle quality of wheelchair users, as well as enhancing their quality of life.Trial registration number ClinicalTrials.gov (NCT04180267)

Role of monoamine oxidases in the exaggerated 5-hydroxytryptamine-induced tension development of human isolated preeclamptic umbilical artery

We investigated the role(s) of monoamine oxidases (MAOs) on the altered 5-hydroxytryptamine (5-HT, serotonin)-induced tension development of the isolated umbilical artery of preeclamptic pregnancy of Chinese women. An enhanced 5-HT-induced tension development of the umbilical artery of preeclamptic pregnancy was observed when compared with that of normal pregnancy. The enhanced component of 5-HT-induced tension development was eradicated by clorgyline (a MAO-A inhibitor). Blockade of eNOS (endothelial isoform nitric oxide synthase) (Nω-nitro-l-arginine methyl ester), 5-HT transporter (citalopram), 5-HT receptor subtypes (5HT2B, SB 204741; 5-HT2C, RS 102221; 5-HT7, SB 269970), and endothelium denudation of the umbilical artery of normal pregnancy mimicked the enhanced 5-HT-induced tension development as observed in the preeclamptic tissues. In contrast, no apparent changes in 5-HT-induced tension development of the umbilical artery of preeclamptic pregnancy were observed with the same pharmacological manipulations. A decreased protein expression levels of MAO-A and eNOS (no iNOS and MAO-B expression was detected) and no change in caveolin-1 and 5-HT transporter expression were demonstrated in the umbilical artery (endothelium intact) lysate of preeclamptic pregnancy, compared to that of the umbilical artery of normal pregnancy. Thus, in the umbilical artery of preeclamptic pregnancy, a decrease of MAO-A and eNOS protein expression levels are probably associated with, or responsible for, the exaggerated 5-HT-induced tension development

Acute Inflammatory Response in Osteoporotic Fracture Healing Augmented with Mechanical Stimulation is Regulated In Vivo through the p38-MAPK Pathway

Low-magnitude high-frequency vibration (LMHFV) has previously been reported to modulate the acute inflammatory response of ovariectomy-induced osteoporotic fracture healing. However, the underlying mechanisms are not clear. In the present study, we investigated the effect of LMHFV on the inflammatory response and the role of the p38 MAPK mechanical signaling pathway in macrophages during the healing process. A closed femoral fracture SD rat model was used. In vivo results showed that LMHFV enhanced activation of the p38 MAPK pathway at the fracture site. The acute inflammatory response, expression of inflammatory cytokines, and callus formation were suppressed in vivo by p38 MAPK inhibition. However, LMHFV did not show direct in vitro enhancement effects on the polarization of RAW264.7 macrophage from the M1 to M2 phenotype, but instead promoted macrophage enlargement and transformation to dendritic monocytes. The present study demonstrated that p38 MAPK modulated the enhancement effects of mechanical stimulation in vivo only. LMHFV may not have exerted its enhancement effects directly on macrophage, but the exact mechanism may have taken a different pathway that requires further investigation in the various subsets of immune cells

Transformative Power of Language Policies in Higher Education:

There is a disjuncture between the intentions of the South African constitutional provisions as contained in Section 6, which prescribe that all eleven official languages be treated equitably, and what is happening on the ground at the institutions of higher learning. In these institutions, English remains the primary language of instruction. African languages are encouraged as support languages in certain instances, but no institution has an African language as a medium of instruction (see for example the Rhodes Language Policy, 2019). This chapter calls into question the use of language and argues for purposive interpretation and practical implementation of the constitutional provisions on language. A proper and linguistically sound understanding of multilingualism as a developmental resource–educational, legal and societal–needs to emerge (Wolff, 2016). The constitutional aspects are explicated in the legislative and policy frameworks discussed in this chapter