Category: 3. Business

Bitcoin and Crypto Stocks Surge as Powell’s Rate-Cut Hint Revives Risk Appetite
Key Takeaways

Crypto market traders were positioned for Jerome Powell to be hawkish in a highly anticipated speech, but they were pleasantly surprised as the Fed chief suggested rate cuts could be coming soon.

Prediction markets bettors now place higher odds of a quarter percentage rate cut in September than they did prior to the speech.

Bitcoin and proxy stocks such as Strategy and Coinbase also surged as the prospect of lower interest rates revived investors’ appetite for risk.
Crypto investors cheered after Federal Reserve Chair Jerome Powell’s speech in Jackson Hole on Friday, latching onto the possibility of a rate cut as soon as next month.

Bitcoin (BTCUSD) was trading at around $116,500 recently, up from a low this morning below $112,000. Bitcoin-proxy stocks also gained, with major bitcoin buyer Strategy (MSTR) rising more than 5% and crypto exchange Coinbase (COIN) up nearly 7%. Altcoins including ethereum (ETDUSD) and solana (SOLUSD) also rose.

While the Fed has held rates steady throughout the year, citing a solid jobs market and concerns over tariff-fueled inflation, Powell now seems open to change. “Overall, while the labor market appears to be in balance, it is a curious kind of balance that results from a marked slowing in both the supply of and demand for workers. This unusual situation suggests that downside risks to employment are rising,” Powell said in prepared remarks Friday morning. He also said “the shifting balance of risks may warrant adjusting our policy stance.”

Fundstrat’s Tom Lee took to social media saying: “Fed Powell speech interpreted as ‘dovish’ as we expected.”

Earlier in the week, crypto traders had positioned for an upset, selling U.S. spot bitcoin in anticipation of more-hawkish comments from the Fed chair. Momentum behind the world’s largest cryptocurrency returned on the prospect of lower rates spurring investor appetites for risk assets.

Crypto natives’ expectations that the Fed would lower its target rate in September had diverged from traditional finance investors’ bets prior to the Jackson Hole speech, but now are more in line with them.

As of Friday afternoon, bettors on prediction markets platform Polymarket placed about an 80% chance of a quarter-point cut next month; before the market’s open, odds were at 56%. CME FedWatch now shows an 87% probability compared to 75% yesterday.
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August 22, 2025
Efficacy and safety of B/F/TAF in treatment-naïve and virologically suppressed people with HIV ≥ 50 years of age: integrated analysis from six phase 3 clinical trials | BMC Infectious Diseases

Participant demographic and baseline characteristics

In this pooled analysis of 2 cohorts from 6 studies that assessed B/F/TAF in participants ≥ 50 years of age (compared with participants < 50 years of age), baseline characteristics were evaluated across both treatment-naïve and virologically suppressed cohorts (Table 1).

Table 1 Demographics and characteristics

Among treatment-naïve participants, the median age was 55 years (quartile [Q]1, Q3: 52, 60) for those ≥ 50 years of age (n = 96) and 30 years (Q1, Q3: 25, 37) for those < 50 years of age (n = 538). This cohort included 5 participants ≥ 65 years of age, 91 participants 50 to < 65 years of age, and 538 participants < 50 years of age, for a total of 634 participants in the B/F/TAF analysis set. The majority of participants were male at birth, with 84.4% in the ≥ 50 years of age group and 90.0% in the < 50 years of age. Regionally, 58.3% of participants ≥ 50 years of age and 67.8% of those < 50 years of age were from the United States, with the remainder from other countries. Racially, 61.5% of participants ≥ 50 years of age and 56.5% of those < 50 years of age identified as White, while 31.3% and 33.7%, respectively, identified as Black. Hispanic or Latino ethnicity was reported by 11.5% of participants ≥ 50 years of age and 26.9% of those < 50 years of age. Median baseline HIV-1 RNA level was 4.48 log₁₀ copies/mL (Q1, Q3: 4.00, 4.93) for participants ≥ 50 years of age and 4.41 log₁₀ copies/mL (Q1, Q3: 4.00, 4.86) for those < 50 years of age. CD4 T-cell counts were similar, with a median of 436 cells/µL (Q1, Q3: 235, 601) in the ≥ 50 years of age group and 442 cells/µL (Q1, Q3: 299, 590) in the < 50 years of age group. Among participants ≥ 50 years of age, 16.7% had diabetes, 6.3% had cardiovascular disease, 40.6% had hyperlipidemia, and 47.9% had hypertension. Among participants < 50 years of age, 4.1% had diabetes, 1.5% had cardiovascular disease, 8.9% had hyperlipidemia, and 9.7% had hypertension (Table 1).

For the virologically suppressed cohort, the median age was 56 years (Q1, Q3: 52, 60) for participants ≥ 50 years of age (n = 450) and 39 years (Q1, Q3: 33, 45) for those < 50 years of age (n = 640). This cohort included 54 participants ≥ 65 years of age, 396 participants 50 to < 65 years of age, and 640 participants < 50 years of age, for a total of 1090 in the B/F/TAF safety analysis set. Most participants were male at birth, with 76.0% in the ≥ 50 years of age group and 61.4% in the < 50 years of age group. In terms of region, 72.7% of those ≥ 50 years of age and 45.9% of those < 50 years of age were from the United States. Among those ≥ 50 years of age, 64.7% of participants identified as White, 29.1% identified as Black, and 16.0% reported Hispanic or Latino ethnicity. In the < 50 years of age group, 57.7% of participants identified as White, 25.9% identified as Black, and 20.5% reported Hispanic or Latino ethnicity. Baseline virologic suppression was high, with 98.0% (441/450) of those ≥ 50 years of age and 98.8% (632/640) of those < 50 years of age having HIV-1 RNA < 50 copies/mL. Median CD4 T-cell counts were 640 cells/µL (Q1, Q3: 486, 852) for participants ≥ 50 years of age and 691 cells/µL (Q1, Q3: 523, 887) for those < 50 years of age. Medical history showed a notable prevalence of comorbidities among those ≥ 50 years of age: 11.1% of participants had cardiovascular disease, 16.9% had diabetes, 48.9% had hyperlipidemia, and 40.4% had hypertension. Among those < 50 years of age, 2.3% of participants had cardiovascular disease, 5.9% had diabetes, 18.9% had hyperlipidemia, and 17.3% had hypertension (Table 1).

Virologic Outcomes

In the treatment-naïve cohort, virologic suppression (HIV-1 RNA < 50 copies/mL) at Week 240 was achieved by 98.5% of participants (67/68) ≥ 50 years of age (95% CI: 92.1%−100.0%) and by 98.6% of participants (359/364) < 50 years of age (95% CI: 96.8%−99.6%), as determined by the M = E analysis (P = 0.9139; Fig. 1A). In the M = F analysis, virologic suppression at Week 240 was maintained by 69.8% of participants (67/96) ≥ 50 years of age (95% CI: 59.6%−78.7%) and by 66.7% of participants (359/538) < 50 years of age (95% CI: 62.6%−70.7%), with no significant differences observed between the 2 age groups (P = 0.50).

Fig. 1

Virologic outcomes (M = E) in the treatment-naïve (A) and virologically suppressed (B) cohorts. B/F/TAF, bictegravir/emtricitabine/tenofovir alafenamide; c, copies; HIV-1, human immunodeficiency virus–1; M = E, missing = excluded. Results are shown using the M=E approach unless otherwise indicated. A Treatment-naïve cohort: the rate of virologic suppression (HIV-1 RNA<50 c/mL) with B/F/TAF was similar at Week 240 between age groups (M = E). B Virologically suppressed cohort: the rate of virologic failure (HIV-1 RNA ≥50 c/mL) was low and virologic suppression (HIV-1 RNA <50 c/mL) was high with B/F/TAF at Week 48 in both age groups (FDA Snapshot)

At Week 48 in the virologically suppressed cohort, virologic suppression (HIV-1 RNA < 50 copies/mL), as assessed by the FDA snapshot method, was achieved by 93.6% of participants, with 93.6% of participants (421/450; 95% CI: 90.9%−95.6%) ≥ 50 years of age and 93.6% of participants (599/640; 95% CI: 91.4%−95.4%) < 50 years of age achieving suppression; there were no statistically significant differences between the 2 groups (P = 1.00; Fig. 1B). The proportion of participants with HIV-1 RNA ≥ 50 copies/mL was 0.9% (4/450; 95% CI: 0.2%−2.3%) for participants ≥ 50 years of age and 1.4% (9/640; 95% CI: 0.6%−2.7%) for those < 50 years of age, with no statistically significant differences between the 2 groups (P = 0.58; Fig. 1B). At Week 48, virologic suppression (HIV-1 RNA < 50 copies/mL) was achieved by 99.3% of participants (426/429; 95% CI: 98.0%−99.9%) ≥ 50 years of age and 98.7% of participants (602/610; 95% CI: 97.4%−99.4%) < 50 years of age in the M = E analysis. The difference between the 2 age groups was not statistically significant (P = 0.54). At Week 48, virologic suppression (HIV-1 RNA < 50 copies/mL) was achieved by 94.7% of participants (426/450; 95% CI: 92.2%−96.6%) ≥ 50 years of age and 94.1% of participants (602/640; 95% CI: 91.9%−95.8%) < 50 years of age in the M = F analysis. The difference between the 2 age groups was not statistically significant (P = 0.69). No treatment-emergent resistance to B/F/TAF was observed in either age group through Week 240 in the treatment-naïve cohort or through Week 48 in the virologically suppressed cohort.

CD4 T-cell counts

At Week 240 in the treatment-naïve cohort, CD4 T-cell counts continued to increase from baseline in both age groups (mean [SD] change from baseline: +291 [221.3] for participants ≥ 50 years of age and + 347 [238.2] for those < 50 years of age; least squares mean difference [LSMD]: − 58 [range: − 120, 4]; P = 0.07). At Week 48 in the virologically suppressed cohort, CD4 T-cell count also increased from baseline among participants ≥ 50 years of age and those < 50 years of age (mean [SD] change from baseline: +18 [162.5] and + 4 [174.9] cells/µL, respectively; LSMD: 15 [range: − 7, 36]; P = 0.18), indicating no statistically significant difference between age groups.

Adherence

Adherence to B/F/TAF through Week 240 for the treatment-naïve cohort and through Week 48 for the virologically suppressed cohort is shown in Table 2. In the treatment-naïve cohort, the median adherence rate was high in both age groups, with participants ≥ 50 years of age showing a median (Q1, Q3) adherence of 98.2% (96.7, 99.4) and those < 50 years of age at 97.0% (93.2, 98.9). A greater proportion of participants ≥ 50 years of age achieved an adherence rate of ≥ 95% compared with those < 50 years of age (82.8% vs. 66.3%, respectively; P = 0.002; Table 2). In the virologically suppressed cohort, the median adherence rate was also high for both age groups at Week 48, with a median (Q1, Q3) adherence of 98.8% (96.8, 99.7) for participants ≥ 50 years of age and 98.8% (96.8, 99.7) for those < 50 years of age. The proportion of participants with adherence rates of ≥ 95%, ≥ 85 to < 95%, and < 85% was similar between age groups, indicating consistent adherence across age categories in the virologically suppressed cohort (Table 2).

Outcomes in body weight, lipid profile, renal function, and bone health

Change from baseline in body weight through Week 240 is shown for participants ≥ 50 and < 50 years of age in the treatment-naïve cohort and through Week 48 for the virologically suppressed cohort. In the treatment-naïve cohort, there were no significant overall differences in weight change at Week 240 between participants ≥ 50 years of age and those < 50 years of age. The median (Q1, Q3) weight gain was 4.8 kg (0.7, 10.2) for participants ≥ 50 years of age and 6.4 kg (2.4, 12.0) for those < 50 years of age. At Week 48 in the virologically suppressed cohort, change in body weight was minimal, with a median (Q1, Q3) weight gain of 1.5 kg (–0.8, 3.8) for participants ≥ 50 years of age and 1.8 kg (–0.4, 4.0) for those < 50 years of age. No significant difference in weight change was observed between age groups in either cohort (Table 3 and Supplement Table 1).

Table 3 Bone, renal, and metabolic outcomes

Change from baseline in fasting lipid parameters, specifically the TC: HDL ratio, was assessed across both age groups. At Week 240 in the treatment-naïve cohort, the median (Q1, Q3) change from baseline in TC: HDL ratio was − 0.3 (–0.9, 0.4) for participants ≥ 50 years of age and 0.1 (–0.4, 0.6) for those < 50 years of age. At Week 48 in the virologically suppressed cohort, change in the TC: HDL ratio was minimal, with a median (Q1, Q3) change of − 0.1 (–0.5, 0.4) for participants ≥ 50 years of age and 0.0 (–0.4, 0.3) for those < 50 years of age (Table 3). Additionally, the proportion of participants who initiated lipid-modifying agents during the study was higher among those ≥ 50 years of age compared with those < 50 years of age (5.3% vs. 0.8%, respectively; P < 0.0001).

Change from baseline in eGFR was similar across age groups in both the treatment-naïve and virologically suppressed cohorts. At Week 240 in the treatment-naïve cohort, the median (Q1, Q3) change in eGFR from baseline was − 10.5 mL/min (–19.6, 2.4) for participants ≥ 50 years of age and − 7.7 mL/min (–19.4, 3.0) for those < 50 years of age, with no statistically significant difference between age groups (P = 0.30). At Week 48 in the virologically suppressed cohort, the median (Q1, Q3) change in eGFR was − 0.9 mL/min (–8.1, 5.8) for participants ≥ 50 years of age and − 1.0 mL/min (–9.6, 8.4) for those < 50 years of age (P = 0.92), indicating minimal change and no significant difference across age groups (Table 3).

Change from baseline in BMD was minimal and similar between age groups in both cohorts. At Week 240 in the treatment-naïve cohort, the mean (SD) percent change in hip BMD was 0.3% (3.26%) for participants ≥ 50 years of age and − 0.4% (5.80%) for those < 50 years of age, while mean (SD) percent change in spine BMD was 1.3% (5.64%) and − 0.9% (4.95%), respectively. At Week 48 in the virologically suppressed cohort, the mean (SD) percent change in hip BMD was 0.2% (2.43%) for participants ≥ 50 years of age and 0.1% (2.03%) for those < 50 years of age; mean percent change in spine BMD increased by 0.5% (3.52%) and 0.8% (2.78%), respectively. No significant difference was observed between age groups in either cohort (Fig. 2).

Fig. 2

Percent change from baseline in BMD in the treatment-naïve (A) and virologically suppressed (B) cohorts. BMD, bone mineral density; SD, standard deviation. A Treatment-naïve cohort: mean percent change in hip and spine BMD from baseline to Week 240 in participants ≥50 years of age and <50 years of age. B Virologically suppressed cohort: mean percent change in hip and spine BMD from baseline to Week 48 in participants≥50 years of age and <50 years of age

Other safety outcomes

In the treatment-naïve cohort, TEAEs were reported by 93.8% of participants ≥ 50 years of age and by 95.5% of those < 50 years of age, with study drug–related TEAEs in 26.0% and 28.4% of participants, respectively. Grade 3 or 4 TEAEs affected 31.3% of participants ≥ 50 years of age and 19.0% of those < 50 years of age, with serious TEAEs in 34.4% and 19.1% of participants, respectively. Discontinuation due to TEAEs occurred in 4.2% and 1.1% of participants in the ≥ 50 and < 50 years of age groups, respectively, with 6 deaths (6.3%) among those ≥ 50 years of age and 2 deaths (0.4%) among those < 50 years of age. In the virologically suppressed cohort, TEAEs were observed in 78.7% of participants ≥ 50 years of age and in 77.2% of those < 50 years of age, with study drug–related TEAEs in 12.9% and 12.5% of each group, respectively. Grade 3 or 4 TEAEs were seen in 7.3% and 4.7% of participants ≥ 50 and < 50 years of age, respectively, with serious TEAEs in 8.7% and 4.7%. Discontinuation due to TEAEs occurred in 1.8% of participants ≥ 50 years of age and 0.9% of those < 50 years of age, with 2 deaths reported in each age group (0.4% and 0.3%, respectively; Table 4).

Treatment-emergent diabetes and hypertension

Treatment-emergent (events that occur while on study) diabetes and hypertension through Week 240 for the treatment-naïve cohort and through Week 48 for the virologically suppressed cohort are shown in Table 5. From baseline to Week 240, treatment-emergent diabetes was observed in 5.1% of participants ≥ 50 years of age and 1.7% of those < 50 years of age (P = 0.08). Treatment-emergent hypertension was reported in 19.6% of participants ≥ 50 years of age and 12.5% of those < 50 years of age (P = 0.19). At Week 48 in the virologically suppressed cohort, the incidence of treatment-emergent diabetes was 1.1% in participants ≥ 50 years of age and 1.3% in those < 50 years of age (P = 1.00), while treatment-emergent hypertension was observed in 5.2% of participants ≥ 50 years of age and 2.6% of those < 50 years of age (P = 0.07). No significant difference in rates of treatment-emergent diabetes or hypertension was found between age groups in either cohort (Table 5).

Table 5 Treatment-emergent diabetes and hypertension

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August 22, 2025
Get Yamaha Bike Now, Pay Later with 0% Mark-Up from SCB Get Yamaha Bike Now, Pay Later with 0% Mark-Up from SCB
In an effort to make personal transport more accessible, Standard Chartered Bank has launched a financing option for Yamaha motorcycles in Pakistan. The bank’s Aasan Instalment Plan allows credit card holders to purchase any Yamaha bike with 0% mark-up, spreading payments over 12 months. This initiative aims to ease the financial burden on buyers, making Yamaha motorcycles more affordable.

Offer Details:
- No interest charges during the 12-month tenure.
- Available on all Yamaha bike models.
- Flexible payment options with zero processing fee for the 0% markup.
The offer provides a budget-friendly way for individuals to buy a Yamaha motorcycle without the usual financing charges. Standard Chartered credit card holders can apply through the bank’s helpline and enjoy easy monthly payments.

Competition in the Market:

While Yamaha offers this no-interest plan, competitors like Honda and Suzuki are also stepping up with installment-based schemes. However, Yamaha’s 0% mark-up on their plan distinguishes it from others, providing a significant advantage in the budget-conscious market.

How to Apply:

Eligibility: Must be a Standard Chartered credit card holder in good standing.

Customers can reach out to Standard Chartered’s helpline at 021-111-002-002 for more details and to place orders.

This limited-time offer underscores the growing demand for affordable financing options and sets Yamaha ahead in the competitive motorcycle market.
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August 22, 2025

JS Bank Half-Year Profit Slumps 45% Amid Margin Pressure and Rising Costs JS Bank Profit Slumps 45% in 1HFY25 Amid Rising Costs

JS Bank Limited (PSX: JSBL) reported a sharp decline in earnings for 1HFY25, with consolidated profit after tax falling 45.1% YoY to Rs5.32 bn. Earnings per share dropped to Rs1.99 from Rs3.86 a year earlier, according to the bank’s filing with the PSX.

Key Drivers:

Margin compression: Net mark-up income fell 8.3% to Rs32.45 bn, reflecting lower asset yields despite reduced funding costs.

Non-mark-up income gains: Jumped 40.8% to Rs12.51 bn, mainly due to Rs4.63 bn in securities gains (+281% YoY).

Foreign-exchange income decline: FX revenue fell 61%, reducing a historically important buffer.

Rising costs: Operating expenses surged 26.7% to Rs30.18 bn, pushing the cost-to-income ratio to ~68%.

Higher credit charges: Credit loss allowance jumped 79.8% to Rs2.28 bn, further pressuring profitability.

Pre-tax profit: Declined 36.4% to Rs11.93 bn.

The results highlight underlying pressure on core banking income, with trading gains cushioning the decline. Investors remain cautious, given the reliance on volatile income sources amid rising costs.

JS Bank 1HFY25 Consolidated Figures (vs 1HFY24)

Metric	1HFY25	1HFY24	Change
PAT	Rs5.32 bn	Rs9.70 bn	-45.1%
EPS	Rs1.99	Rs3.86	-48.4%
Net Mark-up Income	Rs32.45 bn	Rs35.38 bn	-8.3%
Non-Mark-up Income	Rs12.51 bn	Rs8.88 bn	+40.8%
Gains on Securities	Rs4.63 bn	Rs1.22 bn	+281%
Operating Expenses	Rs30.18 bn	Rs23.82 bn	+26.7%
Credit Loss Allowance	Rs2.28 bn	Rs1.27 bn	+79.8%
Pre-tax Profit	Rs11.93 bn	Rs18.75 bn	-36.4%

JS Bank’s half-year performance underscores margin pressure, rising costs, and higher credit charges. Future profitability will depend on restoring core income growth and managing expenses without over-reliance on trading gains.

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August 22, 2025

Natural Alkaloids From Mitragyna speciosa Show Potential as Novel HER2 Inhibitors in Breast Cancer

Human epidermal growth factor receptor 2 (HER2)-positive breast cancer represents approximately 20% of breast cancer diagnoses worldwide and is historically associated with aggressive disease biology and poorer prognoses. Even as therapies like trastuzumab (Herceptin; Genentech), pertuzumab (Perjeta; Genentech), and other tyrosine kinase inhibitors have revolutionized outcomes, challenges with drug resistance and harmful effects still pose issues. This leads to a need for novel therapeutic approaches.¹

Natural products, long regarded as a source of drug discovery, are now being re-examined. A new study published in Current Research in Structural Biology explored the anti-HER2 effect of two alkaloids derived from Mitragyna speciosa (or kratom), mitragynine and 7-hydroxymitragynine (7-OH). They used tests, including molecular docking, molecular dynamics simulations, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiling, to explore these possible effects.¹

Image Credit: Yonus | stock.adobe.com

The investigators reported that both mitragynine and 7-OH demonstrated stable binding affinity with the HER2 receptor. Docking analysis showed binding energies of –7.56 kcal/mol for mitragynine and –8.77 kcal/mol for 7-OH, with interactions observed at key residues such as Leu726, Val734, Ala751, Lys753, Thr798, and Asp863.¹ These results suggest that both compounds could effectively occupy HER2’s active site, potentially disrupting oncogenic signaling pathways. Molecular dynamics simulations demonstrated the stability of all 3 complexes, including mitragynine, 7-OH, and native (SYR127063), over the simulation period.¹

Further binding free energy validation using the MM-PBSA method supported these findings. SYR127063, the control variable, exhibited the strongest binding, while mitragynine and 7-OH showed moderately strong affinities. Importantly, both compounds satisfied multiple drug-likeness rules, including Lipinski, Ghose, Veber, Egan, and Muegge filters, and demonstrated favorable ADMET properties, indicating their potential as viable lead molecules for further preclinical investigation.^1,2

These results align with broader work in oncology to use natural compounds as cancer-fighting tools. Past research has shown the worth of plant-based chemicals as sources of bioactive molecules that can modulate cell signaling, apoptosis, and angiogenesis in cancer models. The chance for mitragynine and its byproduct to act as HER2 blockers is notable, given how much dependence there is on biologic treatments and the need for small-molecule alternatives that may cost less and be more accessible.

For pharmacists, these early-stage findings are important. Although the data remain strictly computational and require extensive laboratory validation, they highlight the role of computational drug discovery in oncology. Pharmacists should be aware of new natural product-based therapies, as these compounds may soon enter preclinical tests and lead to new oral HER2 inhibitors. Since mitragynine and 7-OH are main compounds of kratom, pharmacists must also keep an eye on regulatory considerations and the need to differentiate between recreational kratom use and carefully derived pharmaceutical applications.

Mitragynine and 7-OH from Mitragyna speciosa demonstrate promising early results in silico activity against HER2 and possess favorable drug traits and pharmacokinetic profiles. Although these findings are not ready for clinical recommendations, they set the base for further laboratory validation and may lead to new drug development. If these compounds work well in future clinical studies, they may represent an innovative natural product–based strategy for overcoming resistance and improving outcomes in HER2-positive breast cancer.

REFERENCES

1. Akbar NH, Suarantika F, Fakih TM, Haniffadli A, Muslimawati K, Putra AMP. Screening, docking, and molecular dynamics analysis of Mitragyna speciosa (Korth.) compounds for targeting HER2 in breast cancer. Curr Res Struct Biol. 2025;10:100171. Published 2025 Jun 20. doi:10.1016/j.crstbi.2025.100171

2. Ryan BM, Faupel-Badger JM. The hallmarks of premalignant conditions: a molecular basis for cancer prevention. Semin Oncol. 2016;43(1):22-35. doi:10.1053/j.seminoncol.2015.09.007

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August 22, 2025
Pop Mart rolling out mini Labubus and a long-fur version of the popular plush toy

China’s Pop Mart says it is rolling out a mini version of its popular Labubu plush toys this month, along with a new long-fur version of the toothy little monster.

The Labubu, by artist and illustrator Kasing Lung, first appeared with pointed ears and pointy teeth, in three picture books inspired by Nordic mythology in 2015.

In 2019 Lung struck a deal with Pop Mart, a company that caters to toy connoisseurs and influencers, to sell Labubu figurines. But it wasn’t until Pop Mart started selling Labubu plush toys on key rings in 2023 that the toothy monsters suddenly seemed to be everywhere.

Pop Mart said Friday that the mini-sized Labubu vinyl plush pendant, which is part of The Monsters Pin For Love series, will be available in various colors corresponding to letters of the alphabet. They will cost $22.99 each.

The series also includes 30 letter pendant blind boxes, each with a unique pattern and Monsters charm. They will be priced at $18.99 a piece.

In addition, Pop Mart is launching the Rock the Universe vinyl plush doll, which is part of The Monsters Big Into Energy Series. The plush, which will have a pearl-and-alloy heart necklace, will be the first of the Monsters to have long fur and uses a specialized dyeing technique that ensures no two figures are exactly alike. The dolls will cost $114.99 each.

All of the new products will be available starting Aug. 29 on Pop Mart’s website either for in-store pickup or shipping. They will also be available on the company’s app and its official TikTok accounts.

Labubu has been a bonanza for Pop Mart. Its revenue more than doubled in 2024 to 13.04 billion yuan ($1.81 billion), thanks in part to its elvish monster. Revenue from Pop Mart’s plush toys soared more than 1,200% in 2024, nearly 22% of its overall revenue, according to the company’s annual report.

Earlier this week Pop Mart reported that its profit attributable to shareholders skyrocketed almost 400% for the first six months of the year. Revenue jumped more than 200% to 13.88 billion yuan ($1.93 billion). Revenue for the Asia Pacific region surged more than 250%, while revenue for the Americas soared more than 1,000%.

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August 22, 2025
Local Models, Slipping Sales, and a Silent Software Shift

This article first appeared on GuruFocus.

Tesla (NASDAQ:TSLA) is quietly switching gears in Chinaand it’s not just about batteries or bodywork. To keep pace with domestic EV rivals, the automaker is now integrating homegrown artificial intelligence from Bytedance’s Doubao and Deepseek into its in-car experience. The move gives drivers access to voice-controlled navigation, entertainment, and cabin controlsall hosted on Bytedance’s Volcano Engine cloud. It’s the kind of localized tech that Chinese consumers have come to expect, especially with competitors like BYD and Geely already layering Deepseek into their systems.

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What’s missing? Grokthe conversational AI built by Elon Musk’s xAI and embedded in U.S. Teslas. China’s regulatory landscape has likely made it tough for Tesla to bring Grok across the border. Instead, it’s following a playbook similar to BMW, which recently tapped Alibaba’s QWen to power voice functions in its China-focused lineup. It’s part of a broader trend: foreign carmakers leaning into local AI to meet the expectations of a hyper-digitized consumer base in the world’s most advanced EV market.

But under the surface, there’s another story unfolding. Shipments from Tesla’s Shanghai plant have declined in six of the first seven months this year, with July deliveries down 8.4% from a year ago. The company hasn’t confirmed whether the new AI features are live in vehicles yet, and several owners told Bloomberg they haven’t seen updates. With the last official over-the-air update logged on August 18, investors are left wondering whether this AI pivot is a defensive moveor the beginning of Tesla’s next chapter in China.

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August 22, 2025
Fitch Affirms Sherwin-Williams' IDR at 'BBB+'; Outlook Stable – Fitch Ratings
1. Fitch Affirms Sherwin-Williams’ IDR at ‘BBB+’; Outlook Stable Fitch Ratings
2. Sherwin-Williams stock rating reiterated at Outperform by RBC Capital Investing.com
3. Sherwin-Williams (SHW) Up 7.1% Since Last Earnings Report: Can It Continue? Yahoo Finance
4. Sherwin-Williams: Navigating Market Challenges with Strategic Positioning and Market Share Opportunities TipRanks
5. This Sherwin-Williams Analyst Is No Longer Bearish; Here Are Top 5 Upgrades For Thursday Benzinga
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August 22, 2025
Predictive value of subgenual cingulate normative connectivity to TMS treatment site for antidepressant response in routine clinical practice: a prospective, multisite cohort study
Connolly KR, Helmer A, Cristancho MA, Cristancho P, O’Reardon JP. Effectiveness of transcranial magnetic stimulation in clinical practice post-FDA approval in the United States: results observed with the first 100 consecutive cases of depression at an academic medical center. J Clin Psychiatry. 2012;73:e567–e573. https://doi.org/10.4088/JCP.11m07413

Article
PubMed

Google Scholar
Trapp NT, Bruss J, King Johnson M, Uitermarkt BD, Garrett L, Heinzerling A, et al. Reliability of targeting methods in TMS for depression: beam F3 vs. 5.5 cm. Brain Stimul. 2020;13:578–81. https://doi.org/10.1016/j.brs.2020.01.010

Article
PubMed
PubMed Central

Google Scholar
Weigand A, Horn A, Caballero R, Cooke D, Stern AP, Taylor SF, et al. Prospective validation that subgenual connectivity predicts antidepressant efficacy of transcranial magnetic stimulation sites. Biol Psychiatry. 2018;84:28–37. https://doi.org/10.1016/j.biopsych.2017.10.028

Article
CAS
PubMed

Google Scholar
Cash RFH, Zalesky A, Thomson RH, Tian Y, Cocchi L, Fitzgerald PB. Subgenual functional connectivity predicts antidepressant treatment response to transcranial magnetic stimulation: independent validation and evaluation of personalization. Biol Psychiatry. 2019;86:e5–e7. https://doi.org/10.1016/j.biopsych.2018.12.002

Article
PubMed

Google Scholar
Fox MD, Buckner RL, White MP, Greicius MD, Pascual-Leone A. Efficacy of transcranial magnetic stimulation targets for depression is related to intrinsic functional connectivity with the subgenual cingulate. Biol Psychiatry. 2012;72:595–603. https://doi.org/10.1016/j.biopsych.2012.04.028

Article
PubMed
PubMed Central

Google Scholar
Cash RFH, Weigand A, Zalesky A, Siddiqi SH, Downar J, Fitzgerald PB, et al. Using brain imaging to improve spatial targeting of transcranial magnetic stimulation for depression. Biol Psychiatry. 2021;90:689–700. https://doi.org/10.1016/j.biopsych.2020.05.033

Article
PubMed

Google Scholar
Siddiqi SH, Weigand A, Pascual-Leone A, Fox MD. Identification of personalized transcranial magnetic stimulation targets based on subgenual cingulate connectivity: an independent replication. Biol Psychiatry. 2021;90:e55–e56. https://doi.org/10.1016/j.biopsych.2021.02.015

Article
PubMed

Google Scholar
Fox MD, Liu H, Pascual-Leone A. Identification of reproducible individualized targets for treatment of depression with TMS based on intrinsic connectivity. NeuroImage. 2013;66:151–60. https://doi.org/10.1016/j.neuroimage.2012.10.082

Article
PubMed

Google Scholar
Cole EJ, Stimpson KH, Bentzley BS, Gulser M, Cherian K, Tischler C, et al. Stanford accelerated intelligent neuromodulation therapy for treatment-resistant depression. Am J Psychiatry. 2020;177:716–26. https://doi.org/10.1176/appi.ajp.2019.19070720

Article
PubMed

Google Scholar
Cole EJ, Phillips AL, Bentzley BS, Stimpson KH, Nejad R, Barmak F, et al. Stanford neuromodulation therapy (SNT): a double-blind randomized controlled trial. Am J Psychiatry. 2021;179:132–41. https://doi.org/10.1176/appi.ajp.2021.20101429

Article
PubMed

Google Scholar
Blumberger DM, Vila-Rodriguez F, Thorpe KE, Feffer K, Noda Y, Giacobbe P, et al. Effectiveness of theta burst versus high-frequency repetitive transcranial magnetic stimulation in patients with depression (THREE-D): a randomised non-inferiority trial. Lancet. 2018;391:1683–92. https://doi.org/10.1016/S0140-6736(18)30295-2

Article
PubMed

Google Scholar
Blumberger DM, Mulsant BH, Thorpe KE, McClintock SM, Konstantinou GN, Lee HH, et al. Effectiveness of standard sequential bilateral repetitive transcranial magnetic stimulation vs bilateral theta burst stimulation in older adults with depression: the FOUR-D randomized noninferiority clinical trial. JAMA Psychiatry. 2022;79:1065–73. https://doi.org/10.1001/jamapsychiatry.2022.2862

Article
PubMed
PubMed Central

Google Scholar
Yesavage JA, Fairchild JK, Mi Z, Biswas K, Davis-Karim A, Phibbs CS, et al. Effect of repetitive transcranial magnetic stimulation on treatment-resistant major depression in US Veterans: a randomized clinical trial. JAMA Psychiatry. 2018;75:884–93. https://doi.org/10.1001/jamapsychiatry.2018.1483

Article
PubMed
PubMed Central

Google Scholar
Mir-Moghtadaei A, Siddiqi SH, Mir-Moghtadaei K, Blumberger DM, Vila-Rodriguez F, Daskalakis ZJ, et al. Updated scalp heuristics for localizing the dorsolateral prefrontal cortex based on convergent evidence of lesion and brain stimulation studies in depression. Brain Stimul. 2022;15:291–5. https://doi.org/10.1016/j.brs.2022.01.013

Article
PubMed

Google Scholar
Siddiqi SH, Taylor SF, Cooke D, Pascual-Leone A, George MS, Fox MD. Distinct symptom-specific treatment targets for circuit-based neuromodulation. Am J Psychiatry. 2020;177:435–46. https://doi.org/10.1176/appi.ajp.2019.19090915

Article
PubMed
PubMed Central

Google Scholar
Brakemeier EL, Luborzewski A, Danker-Hopfe H, Kathmann N, Bajbouj M. Positive predictors for antidepressive response to prefrontal repetitive transcranial magnetic stimulation (rTMS). J Psychiatr Res. 2007;41:395–403. https://doi.org/10.1016/j.jpsychires.2006.01.013

Article
PubMed

Google Scholar
Fregni F, Marcolin MA, Myczkowski M, Amiaz R, Hasey G, Rumi DO, et al. Predictors of antidepressant response in clinical trials of transcranial magnetic stimulation. Int J Neuropsychopharmacol. 2006;9:641–54. https://doi.org/10.1017/S1461145705006280

Article
CAS
PubMed

Google Scholar
Lisanby SH, Husain MM, Rosenquist PB, Maixner D, Gutierrez R, Krystal A, et al. Daily left prefrontal repetitive transcranial magnetic stimulation in the acute treatment of major depression: clinical predictors of outcome in a multisite, randomized controlled clinical trial. Neuropsychopharmacology. 2009;34:522–34. https://doi.org/10.1038/npp.2008.118

Article
PubMed

Google Scholar
Liston C, Chen AC, Zebley BD, Drysdale AT, Gordon R, Leuchter B, et al. Default mode network mechanisms of transcranial magnetic stimulation in depression. Biol Psychiatry. 2014;76:517–26. https://doi.org/10.1016/j.biopsych.2014.01.023

Article
PubMed
PubMed Central

Google Scholar
Kimbrell TA, Little JT, Dunn RT, Frye MA, Greenberg BD, Wassermann EM, et al. Frequency dependence of antidepressant response to left prefrontal repetitive transcranial magnetic stimulation (rTMS) as a function of baseline cerebral glucose metabolism. Biol Psychiatry. 1999;46:1603–13. https://doi.org/10.1016/s0006-3223(99)00195-x

Article
CAS
PubMed

Google Scholar
Paillère Martinot ML, Martinot JL, Ringuenet D, Galinowski A, Gallarda T, Bellivier F, et al. Baseline brain metabolism in resistant depression and response to transcranial magnetic stimulation. Neuropsychopharmacology. 2011;36:2710–9. https://doi.org/10.1038/npp.2011.161

Article
CAS
PubMed

Google Scholar
Kalin NH. The critical relationship between anxiety and depression. Am J Psychiatry. 2020;177:365–7. https://doi.org/10.1176/appi.ajp.2020.20030305

Article
PubMed

Google Scholar
American Psychiatric Association. Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: American Psychiatric Association; 2013.
Beam W, Borckardt JJ, Reeves ST, George MS. An efficient and accurate new method for locating the F3 position for prefrontal TMS applications. Brain Stimul. 2009;2:50–54. https://doi.org/10.1016/j.brs.2008.09.006

Article
PubMed
PubMed Central

Google Scholar
Mylius V, Ayache SS, Ahdab R, Farhat WH, Zouari HG, Belke M, et al. Definition of DLPFC and M1 according to anatomical landmarks for navigated brain stimulation: inter-rater reliability, accuracy, and influence of gender and age. NeuroImage. 2013;78:224–32. https://doi.org/10.1016/j.neuroimage.2013.03.061

Article
CAS
PubMed

Google Scholar
McDonald WM, Durkalski V, Ball ER, Holtzheimer PE, Pavlicova M, Lisanby SH, et al. Improving the antidepressant efficacy of transcranial magnetic stimulation: maximizing the number of stimulations and treatment location in treatment-resistant depression. Depress Anxiety. 2011;28:973–80. https://doi.org/10.1002/da.20885

Article
PubMed

Google Scholar
Yeo BTT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol. 2011;106:1125–65. https://doi.org/10.1152/jn.00338.2011

Article
PubMed

Google Scholar
Li J, Kong R, Liègeois R, Orban C, Tan Y, Sun N, et al. Global signal regression strengthens association between resting-state functional connectivity and behavior. NeuroImage. 2019;196:126–41. https://doi.org/10.1016/j.neuroimage.2019.04.016

Article
PubMed

Google Scholar
Kong R, Li J, Orban C, Sabuncu MR, Liu H, Schaefer A, et al. Spatial topography of individual-specific cortical networks predicts human cognition, personality, and emotion. Cereb Cortex. 2019;26:2533–51. https://doi.org/10.1093/cercor/bhy123

Article

Google Scholar
Beck, AT, Steer, RA, & Brown, GK. Beck Depression Inventory–II (BDI-II). APA PsycTests; 1996. https://doi.org/10.1037/t00742-000
Fox MD. Mapping symptoms to brain networks with the human connectome. N Engl J Med. 2018;379:2237–45. https://doi.org/10.1056/NEJMra1706158

Article
CAS
PubMed

Google Scholar
Kroenke, K, Spitzer, RL, & Williams, JBW. Patient Health Questionnaire-9 (PHQ-9). APA PsycTests; 1999. https://doi.org/10.1037/t06165-000
Rush AJ, Carmody T, Reimitz P-E. The Inventory of Depressive Symptomatology (IDS): clinician (IDS-C) and Self-Report (IDS-SR) ratings of depressive symptoms. Int J Methods Psychiatr Res. 2000;9:45–59. https://doi.org/10.1002/mpr.79

Article

Google Scholar
Jacobs, KM. Brodmann’s areas of the cortex. In JS Kreutzer, J DeLuca, & B Caplan (Eds.), Encyclopedia of clinical neuropsychology. Springer; 2011. https://doi.org/10.1007/978-0-387-79948-3_301
Siddiqi SH, Schaper FLWVJ, Horn A, Hsu J, Padmanabhan JL, Brodtmann A, et al. Brain stimulation and brain lesions converge on common causal circuits in neuropsychiatric disease. Nat Hum Behav. 2021;5:1707–16. https://doi.org/10.1038/s41562-021-01161-1

Article
PubMed
PubMed Central

Google Scholar
Oathes DJ, Zimmerman JP, Duprat R, Japp SS, Scully M, Rosenberg BM, et al. Resting fMRI-guided TMS results in subcortical and brain network modulation indexed by interleaved TMS/fMRI. Exp Brain Res. 2021;239:1165–78. https://doi.org/10.1007/s00221-021-06036-5

Article
PubMed
PubMed Central

Google Scholar
Thielscher, A, Antunes, A, & Saturnino, GB. Field modeling for transcranial magnetic stimulation: a useful tool to understand the physiological effects of TMS? In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 222-5). IEEE; 2015. https://doi.org/10.1109/EMBC.2015.7318340
Elbau IG, Lynch CJ, Downar J, Vila-Rodriguez F, Power JD, Solomonov N, et al. Functional connectivity mapping for rTMS target selection in depression. Am J Psychiatry. 2023;180:230–40. https://doi.org/10.1176/appi.ajp.20220306

Article
PubMed
PubMed Central

Google Scholar
Van Essen DC, Ugurbil K, Auerbach E, Barch D, Behrens TE, Bucholz R, et al. The Human connectome project: a data acquisition perspective. NeuroImage. 2012;62:2222–31. https://doi.org/10.1016/j.neuroimage.2012.02.018

Article
PubMed

Google Scholar
Glasser MF, Sotiropoulos SN, Wilson JA, Coalson TS, Fischl B, Andersson JL, et al. The minimal preprocessing pipelines for the Human Connectome Project. NeuroImage. 2013;80:105–24. https://doi.org/10.1016/j.neuroimage.2013.04.127

Article
PubMed

Google Scholar
Fox MD, Zhang D, Snyder AZ, Raichle ME. The global signal and observed anticorrelated resting state brain networks. J Neurophysiol. 2009;101:3270–83. https://doi.org/10.1152/jn.90777.2008

Article
PubMed
PubMed Central

Google Scholar
Jenkinson M, Beckmann CF, Behrens TEJ, Woolrich MW, Smith SM. FSL. NeuroImage. 2012;62:782–90. https://doi.org/10.1016/j.neuroimage.2011.09.015

Article
PubMed

Google Scholar
Hoffmann M, Billot B, Greve DN, Iglesias JE, Fischl B, Dalca AV. SynthMorph: learning contrast-invariant registration without acquired images. IEEE Trans Med Imaging. 2021;41:543–58. https://doi.org/10.1109/TMI.2021.3116879

Article

Google Scholar
Iglesias JE. A ready-to-use machine learning tool for symmetric multi-modality registration of brain MRI. Sci Rep. 2023;13:6657 https://doi.org/10.1038/s41598-023-33781-0

Article
CAS
PubMed
PubMed Central

Google Scholar
Jenkinson M, Smith SM. A global optimization method for robust affine registration of brain images. Med Image Anal. 2001;5:143–56. https://doi.org/10.1016/s1361-8415(01)00036-6

Article
CAS
PubMed

Google Scholar
Jenkinson M, Bannister PR, Brady JM, Smith SM. Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage. 2002;17:825–41. https://doi.org/10.1016/s1053-8119(02)91132-8

Article
PubMed

Google Scholar
Andersson, JLR, Jenkinson, M, & Smith, S Non-linear registration. FMRIB Technical Report TR07JA2.2010
Harrell FE Jr. Regression Modeling Strategies. New York: Springer-Verlag; 2001. https://doi.org/10.1007/978-1-4757-3462-1
James, G, Witten, D, Hastie, T, & Tibshirani, R. An introduction to statistical learning (8th ed.). New York: Springer Science+Business Media;2017. ISBN 978-1-4614-7138-7
Snee, R. Origins of the variance inflation factor as recalled by Cuthbert Daniel. Technical report. Snee Associates;1981.
Glasser GJ, Winter RF. Critical values of the coefficient of rank correlation for testing the hypothesis of independence. Biometrika. 1961;48:444–8. Oxford University Press. https://doi.org/10.2307/2332767

Article

Google Scholar
Spearman C. The proof and measurement of association between two things. Am J Psychol. 1904;15:72–101. https://doi.org/10.2307/1412159

Article

Google Scholar
James, G, Witten, D, Hastie, T, & Tibshirani, R An introduction to statistical learning (8th ed.). Springer Science+Business Media;2017.
James, G, Witten, D, Hastie, T, & Tibshirani, R. An introduction to statistical learning: with applications in R. New York, NY: Springer;2013. (Chapter 3)
Fox, J Applied regression analysis and generalized linear models (3rd ed.). Los Angeles, CA: Sage Publications;2015.
Agresti, A Categorical data analysis (3rd ed.). Hoboken, NJ: Wiley;2018.
Wright, DB, & London, K Modern regression techniques using R: A practical guide. Thousand Oaks, CA: Sage Publications;2009.
Akaike H. A new look at the statistical model identification. IEEE Trans Autom Control. 1974;19:716–23. https://doi.org/10.1109/TAC.1974.1100705

Article

Google Scholar
Meinshausen N, Bühlmann P. Stability selection. J R Stat Soc Ser B. 2010;72:417–73. https://doi.org/10.1111/j.1467-9868.2010.00740.x

Article

Google Scholar
Avesani P, McPherson B, Hayashi S, Caiafa CF, Henschel R, Garyfallidis E, et al. The open diffusion data derivatives: brain data upcycling via integrated publishing of derivatives and reproducible open cloud services. Sci Data. 2019;6:69 https://doi.org/10.1038/s41597-019-0073-y

Article
PubMed
PubMed Central

Google Scholar
Xia M, Wang J, He Y. BrainNet Viewer: a network visualization tool for human brain connectomics. PLOS ONE. 2013;8:e68910 https://doi.org/10.1371/journal.pone.0068910

Article
CAS
PubMed
PubMed Central

Google Scholar
Taylor S, Zvolensky MJ, Cox BJ, Deacon B, Heimberg RG, Ledley DR, et al. Robust dimensions of anxiety sensitivity: development and initial validation of the Anxiety Sensitivity Index-3. Psychol Assess. 2007;19:176–88. https://doi.org/10.1037/1040-3590.19.2.176

Article
PubMed

Google Scholar
Herbsman T, Avery D, Ramsey D, Holtzheimer P, Wadjik C, Hardaway F, et al. More lateral and anterior prefrontal coil location is associated with better repetitive transcranial magnetic stimulation antidepressant response. Biol Psychiatry. 2009;66:509–15. https://doi.org/10.1016/j.biopsych.2009.04.034

Article
PubMed

Google Scholar
Benster, L, Weissman, C, Suprani, F, Toney, K, Afshar, H, Stapper, N, et al. Predictive modeling of response to repetitive transcranial magnetic stimulation in treatment-resistant depression. Research Square. 2024. https://doi.org/10.21203/rs.3.rs-4396926/v1
Li CT, Su TP, Cheng CM, Chen MH, Bai YM, Tsai SJ. Factors associated with antidepressant responses to repetitive transcranial magnetic stimulation in antidepressant-resistant depression. Front Neurosci. 2022;16:1046920 https://doi.org/10.3389/fnins.2022.1046920

Article
PubMed
PubMed Central

Google Scholar
Zandberg LJ, Zang Y, McLean CP, Yeh R, Simpson HB, Foa EB. Change in obsessive-compulsive symptoms mediates subsequent change in depressive symptoms during exposure and response prevention. Behav Res Ther. 2015;68:76–81. https://doi.org/10.1016/j.brat.2015.03.005

Article
PubMed
PubMed Central

Google Scholar
Fox MD, et al. Resting-state networks link invasive and noninvasive brain stimulation across diverse psychiatric and neurological diseases. Proc Natl Acad Sci. 2014;111:E4367–E4375. https://doi.org/10.1073/pnas.1405003111

Article
CAS
PubMed
PubMed Central

Google Scholar
Baer L, Trivedi MH, Huz I, Rush AJ, Wisniewski SR, Fava M. Prevalence and impact of obsessive-compulsive symptoms in depression: a STARD report. J Clin Psychiatry. 2015;76:1668–74. https://doi.org/10.4088/JCP.14m09670

Article
PubMed

Google Scholar
Sheng J, Liu S, Wang Y, Cui R, Zhang X. The link between depression and chronic pain: neural mechanisms in the brain. Neural Plast. 2017;2017:9724371 https://doi.org/10.1155/2017/9724371

Article
CAS
PubMed
PubMed Central

Google Scholar
Bijsterbosch JD, Ansari TL, Smith S, Gauld O, Zika O, Boessenkool S, et al. Stratification of MDD and GAD patients by resting state brain connectivity predicts cognitive bias. NeuroImage Clin. 2018;19:425–33. https://doi.org/10.1016/j.nicl.2018.04.033

Article
PubMed
PubMed Central

Google Scholar
Silk JS, Sequeira SS, Jones NP, Lee KH, Dahl RE, Forbes EE, et al. Subgenual anterior cingulate cortex reactivity to rejection vs. acceptance predicts depressive symptoms among adolescents with an anxiety history. J Clin Child Adolesc Psychol. 2023;52:659–74. https://doi.org/10.1080/15374416.2021.2019048

Article
PubMed

Google Scholar
Tuescher O, Protopopescu X, Pan H, Cloitre M, Butler T, Goldstein M, et al. Differential activity of subgenual cingulate and brainstem in panic disorder and PTSD. J Anxiety Disord. 2011;25:251–7. https://doi.org/10.1016/j.janxdis.2010.09.010

Article
PubMed

Google Scholar
Zhan C, Liu Y, Wu K, Gao Y, Li X. Structural and functional abnormalities in children with attention-deficit/hyperactivity disorder: a focus on subgenual anterior cingulate cortex. Brain Connect. 2017;7:106–14. https://doi.org/10.1089/brain.2016.0444

Article
PubMed
PubMed Central

Google Scholar
Osborne NR, Cheng JC, Rogachov A, Kim JA, Hemington KS, Bosma RL, et al. Abnormal subgenual anterior cingulate circuitry is unique to women but not men with chronic pain. Pain. 2021;162:97–108. https://doi.org/10.1097/j.pain.0000000000002016

Article
PubMed

Google Scholar
Siddiqi SH, Fox MD. Targeting symptom-specific networks with transcranial magnetic stimulation. Biol Psychiatry. 2024;95:502–9. https://doi.org/10.1016/j.biopsych.2023.11.011

Article
PubMed

Google Scholar
Siddiqi S, Philip NS, Palm S, Arulpragasam A, Barredo J, Bouchard H, et al. A potential neuromodulation target for PTSD in Veterans derived from focal brain lesions. Res Sq. 2024. https://doi.org/10.21203/rs.3.rs-3132332/v1

Article
PubMed
PubMed Central

Google Scholar
Brown JC, Kweon J, Sharma P, Siddiqi SH, Isserles M, Ressler KJ. Critically assessing the unanswered questions of how, where, and when to induce plasticity in the posttraumatic stress disorder network with transcranial magnetic stimulation. Biol Psychiatry. 2024;97:392–404. https://doi.org/10.1016/j.biopsych.2024.06.010

Article
PubMed

Google Scholar
Isserles M, Tendler A, Roth Y, Bystritsky A, Blumberger DM, Ward H, et al. Deep transcranial magnetic stimulation combined with brief exposure for posttraumatic stress disorder: a prospective multisite randomized trial. Biol Psychiatry. 2021;90:721–8. https://doi.org/10.1016/j.biopsych.2021.04.019

Article
PubMed

Google Scholar
Spitzer RL, Kroenke K, Williams JB, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166:1092–7. https://doi.org/10.1001/archinte.166.10.1092

Article
PubMed

Google Scholar
Ning L, Makris N, Camprodon JA, Rathi Y. Limits and reproducibility of resting-state functional MRI definition of DLPFC targets for neuromodulation. Brain Stimul. 2019;12:129–38. https://doi.org/10.1016/j.brs.2018.10.004

Article
PubMed

Google Scholar
Cash RFH, Cocchi L, Lv J, Wu Y, Fitzgerald PB, Zalesky A. Personalized connectivity-guided DLPFC-TMS for depression: advancing computational feasibility, precision, and reproducibility. Hum Brain Mapp. 2021;42:4155–72. https://doi.org/10.1002/hbm.25330

Article
PubMed
PubMed Central

Google Scholar
Cash RFH, Cocchi L, Lv J, Fitzgerald P, Zalesky A. Functional magnetic resonance imaging-guided personalization of transcranial magnetic stimulation treatment for depression. JAMA Psychiatry. 2020;78:337–9. https://doi.org/10.1001/jamapsychiatry.2020.3794

Article
PubMed Central

Google Scholar
Fang K, Niu L, Wen B, Liang L, Tian Y, Hou Y, et al. Individualized resting-state functional connectivity abnormalities unveil two major depressive disorder subtypes with contrasting abnormal patterns of abnormality. Transl Psychiatry. 2025;15:45 https://doi.org/10.1038/s41398-025-03268-9

Article
PubMed
PubMed Central

Google Scholar
Wang Q, Akram H, Muthuraman M, Gonzalez-Escamilla G, Sheth SA, Oxenford S, et al. Normative vs. patient-specific brain connectivity in deep brain stimulation. NeuroImage. 2021;224:117307 https://doi.org/10.1016/j.neuroimage.2020.117307

Article
PubMed

Google Scholar
Cohen A, Soussand L, McManus P, Fox M. GSP1000 preprocessed connectome. Harv Dataverse. 2020. https://doi.org/10.7910/DVN/ILXIKS

Article

Google Scholar

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